economics model essays a level

A-Level Economics Model Answers (for Edexcel Past Papers)

Download A-Level Economics Model Answers for Edexcel past papers below for A2 and AS. These candidate responses were extracted from Edexcel exam board’s examiners’ reports and were graded by Edexcel examiners. All new specification Economics papers and their model answers are now available including Paper 1 (Microeconomics), Paper 2 (Macroeconomics) and Paper 3 (Synoptic).

Relevant resources: Download Edexcel A2 Economics past papers for students studying A2 Economics in their second year. Download Edexcel AS Economics past papers for students studying AS Economics in their first year. Visit our Edexcel Economics notes & questions by topic for practicing and revising certain areas of the course.

Economics Model Answers for A2 (Year 2)

Edexcel 2018 Economics A Paper 1 Model Answers Download

Edexcel 2018 Economics A Paper 2 Model Answers Download

Edexcel 2017 Economics A Paper 1 Model Answers Download

Edexcel 2017 Economics A Paper 2 Model Answers Download

Economics Model Answers for AS (Year 1)

Edexcel 2018 AS Economics A Paper 1 Model Answers Download

Edexcel 2017 AS Economics A Paper 1 Model Answers Download

Edexcel 2016 AS Economics A Paper 1 Model Answers Download

Need help with these Edexcel Economics past papers? Ask our Economics Tutor a free question or get a personalised 1 to 1 online lesson with us today. Alternatively, check out our Edexcel Economics revision notes to refresh your memory.

Other Resources

Looking for the full version of the Examiner Reports and more? You can find more Economics past papers and mark schemes on Edexcel’s resource page . Copyright for these examination papers belongs to their respective examination boards but not Qurious Education Ltd.

AQA A-Level Economics Past Papers

Download A2 AQA Economics past papers for Paper 1 (Microeconomics), Paper 2 (Macroeconomics) and Paper 3 (Synoptic) from 2017 to 2019 below. […]

A-Level-Economics-Syllabus-Edexcel-Featured-Image

Economics A-Level Syllabus (Edexcel)

Please see the Economics A-Level syllabus and curriculum for the Edexcel exam board below, extracted directly from the Edexcel A-Level specification for […]

Phillips Curve Notes & Questions (A-Level, IB Economics)

Relevant Exam Boards: A-Level (Edexcel, OCR, AQA, Eduqas, WJEC), IB, IAL, CIE Edexcel Economics Notes Directory | AQA Economics Notes Directory | […]

Demand/Supply-side Policies Notes (A-Level, IB)

Economics Model Essays

Economics Model Essay 1

(a) Distinguish between the concepts of price elasticity of demand, income elasticity of demand and cross elasticity of demand. [10] (b) Discuss the usefulness of the concepts of elasticity of demand to a firm that produces a fashionable product. [15]

Click to Read

Economics Model Essay 2

After reaching a rate of 8.3 percent in 2010, GDP growth in Asia is projected to average nearly 7 percent in both 2011 and 2012, according to the IMF. However, due to the growing unrest in the Middle East sparked by the Egyptian Revolution that began on 25 January 2011, oil prices have started to rise. Discuss how the market for private cars and its related markets in Asia may be affected by the above events. [25]

Economics Model Essay 3

Findings from a variety of studies show that routine consumption of artificial sweeteners such as aspartame, saccharin and sucralose may lead to higher likelihood of heart disease, stroke, diabetes and high blood pressure. Some countries such as the United States and France have imposed a tax on non-diet soft drinks to fight obesity. A rise in the price of sugar has led to an increase in the demand for artificial sweeteners and this has pushed up the prices.

Discuss how the above events would affect consumer expenditure on diet soft drinks and non-diet soft drinks. [25]

Economics Model Essay 4

The production of petrol should be left to market forces. In spite of this, some governments subsidise petrol while others impose a tax on it. Discuss. [25]

Economics Model Essay 5

Discuss the economic effects of a redirection of government subsidy from petrol to education. [25]

Economics Model Essay 6

Discuss whether an increase in savings in Singapore would lead to problems in the economy. [25]

Economics Model Essay 7

Discuss the view that Singapore has few policies to deal with an external shock. [25]

Economics Model Essay 8

“A fall in the terms of trade is undesirable for the economy.” Discuss the statement. [25]

Economics Model Essay 9

Discuss whether globalisation would benefit firms and households. [25]

Economics Model Essay 10

Discuss whether increasing labour productivity in Singapore would be desirable for the economy. [25]

Economics Model Essay 11

(a) Explain how the different characteristics of the market structures of monopolistic competition and oligopoly affect pricing and output decisions. [10] (b) Discuss whether the behaviour of oligopolistic firms is consistent with the objective of profit maximisation. [15]

Economics Model Essay 12

(a) Explain whether the demand for private cars is elastic or inelastic in Singapore with respect to price and income. [10] (b) Discuss the effects of a sharp rise in the prices of Certificates of Entitlement in Singapore on expenditure by consumers on different types of cars. [15]

Economics Model Essay 13

(a) Compare the characteristics of the market structures of monopolistic competition an oligopoly. [10] (b) Discuss whether firms in a highly competitive market are more vulnerable than firms in a less competitive market in a recession. [15]

Economics Model Essay 14

Discuss whether the concepts of elasticity of demand are useful to the government for discouraging the use of private cars. [25]

Economics Model Essay 15

Discuss whether a shift from reliance on foreign workers to improving labour productivity in Singapore would lead to an increase in international competitiveness. [25]

Economics Model Essay 16

(a) Explain why the government subsidises education. [10] (b) Discuss whether a reduction in the subsidy on education is justified. [15]

Economics Model Essay 17

Discuss whether austerity measures in the European Union would adversely affect the Singapore economy. [25]

Economics Model Essay 18

Discuss whether a shift from direct taxes to indirect taxes would improve the current and future standards of living in Singapore. [25]

Economics Model Essay 19

(a) Explain why Singapore chooses to use the exchange rate rather than interest rates as the policy instrument of its monetary policy. [10]

Note: All rights reserved. No part of these publications may be reproduced, stored in a retrieval system, or transmitted by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Economics tutors and teachers who wish to use the materials for teaching may submit a request to Economics Cafe. The content in the economics model essays will be discussed in greater detail in economics tuition .

Answers To Singapore-Cambridge GCE ‘A’ Level Economics Examination Questions

Singapore-cambridge gce ‘a’ level economics essay mark scheme.

Economics Tuition Singapore @ Economics Cafe Principal Economics Tutor: Mr. Edmund Quek

The Economics Tutor
Testimonials
Crash Courses
Headstart Classes in Dec (for students moving on to JC1)
How to Score in A Level Economics Paper 1 on Case Studies
Maximising Your Potential for A Level Economics Essays
Publications
Latest News

A Level Econs Model Essay: Macroeconomic Aims, Issues And Policies

Examine the impact of a weakening currency on a country’s economic performance.

For greater relevance, the response to this answer considered recent economic conditions (i.e. from 2008-2012). We continue to discuss the case of a weakening exchange rate, although we argue towards the end of the essay that the Sing dollar had in fact been gaining strength in recent times. Recent macroeconomic problems may or may not be attributed to changes in the value of the exchange rate.

In recent years, Singapore had to tackle the twin threats of inflation and a global economic slowdown . The root causes of such problems are largely external in nature and cannot be attributed to a weakening of the Sing dollar. However, a weakening of the Sing dollar to boost economic growth may aggravate imported inflation in the country. Furthermore, recent quantitative easing efforts in US have led to pressure on the Sing dollar to appreciate, which may create other macroeconomic problems as well.

Due to the lingering effects of the US financial crisis and the onset of the Eurozone debt crisis, global incomes have been falling. This has resulted in falling purchasing power which triggers a fall in demand for Singapore exports , assuming that the country exports normal goods. The fall in net exports (X-M) from Singapore’s perspective leads to a fall in aggregate demand (AD) and a multiplied fall in national income through the reverse multiplier process. The fall in AD is aggravated by a fall in foreign direct investments since Western investors may need to preserve capital to tackle financial problems in their home countries.

As shown in Fig.1 below, a fall in AD from AD 1 to AD 2 leads to a multiplied fall in real GDP from Y 1 to Y 2 . Given that demand for labour is derived from the demand for goods and services which the hired labour is used to produce, the decrease in actual growth also leads to a rise in cyclical unemployment (demand-deficient unemployment). In addition, potential growth may slow down with lower levels of FDI entering the country.

Notwithstanding the small size of Singapore’s multiplier, the fall in external demand is likely to hit Singapore especially hard, given that domestic demand is small and the country is largely reliant on exports for growth. In addition, Singapore’s balance of payments (BOP) will be adversely affected by the global slowdown. The BOP refers to the record of transactions between a country and the rest of the world over a period of time, and consists mainly of the current account and the capital account. A fall in net exports leads to a deterioration in the current account while a slowdown in FDI worsens the capital account.

While the above macroeconomic problems are due to external circumstances and are not triggered by exchange rate changes, a weakening of the exchange rate can in fact alleviate these macroeconomic problems. While Singapore generally focuses on maintaining a strong currency to mitigate imported inflation, during times of severe recessions, the central bank may maintain a zero-appreciation stance or depreciate the exchange rate slightly to boost net exports. For example, in the wake of the US financial crisis in early 2009, the MAS allowed a depreciation of the currency by about 2% in order to prop up external demand. By depreciating Singapore’s exchange rate, the price of Singapore’s exports in foreign currency will be relatively cheaper while the price of imports will be relatively higher in domestic currency. Assuming Marshall Lerner’s condition holds, where |PED X +PED M |>1, net exports (X-M) rises. This results in a significant increase in AD which results in an improvement in actual growth and a reduction in cyclical unemployment.

As shown in Fig.2, an increase in net exports will lead to an increase in AD from AD 1 to AD 2 . There is also a multiplied increase in national income from Y 1 to Y 2 , signifying actual growth. Given that demand for labour is derived from the demand for goods and services which the hired labour is used to produce, the increase in real output within the economy also leads to a fall in cyclical unemployment. In addition, the BOP position will also improve (through the current account) with a rise in net exports.

However, there is still a limit to which the central bank can rely on a weak currency to stimulate AD without triggering other macroeconomic problems. With a weaker currency, the price of imported raw materials and finished products will increase in local currency. This can lead to lead to imported inflation, which is a form of cost-push inflation which occurs when the cost of production rises in the economy, independent of aggregate demand (AD). Coupled with instability in the Middle East in recent years which has disrupted the global supply of oil, significant inflationary pressures will be generated in Singapore. The rising cost of production can in turn lower export price competitiveness and offset any gains in export sales due to a weaker currency.

Fig.3: Imported inflation

Diagrammatically, this is represented in Fig.3 as an upward shift in aggregate supply (AS) from AS 1 to AS 2 . Given an unchanged level of aggregate demand, firms will raise prices in line with an increase in production costs, leading to a rise in general price level (GPL) from P 1 to P 2 .

Nonetheless, in recent years, the general price level in Singapore has been driven up by a number of domestic pockets of inflation, which are not related to the strength of the Sing dollar. For example, accommodation prices have spiked recently in Singapore due to a combination of factors. First, the low interest rate environment in Singapore has encouraged consumers to borrow to invest in housing. In addition, quantitative easing measures in US have resulted in flows of hot money into Singapore. Some of these funds are directed into property investments, fuelling higher housing prices. Aside from accommodation costs, private transport cost in Singapore has also been rising largely due to the increase in Certificate of Entitlement (COE) premiums as a result of the lower supply of new COE quotas. This is reflective of the government’s efforts to reduce car ownership and tackle congestion on Singapore’s roads.

Separately, instead of depreciating, the Sing dollar has been gaining strength in recent years, which has created other macroeconomic problems. This is a result of hot money inflows arising from quantitative easing measures being undertaken in US. Given the rate at which money supply is increased in the US (US$85bn per month), hot money has been flowing into Singapore in search of better returns. Some of this hot money is directed into property investments while others may flow into the equity market. Aside from causing asset price inflation as discussed earlier, the inflow of hot money results in higher demand for Sing dollar and may result in an appreciation of the currency.

An appreciation of the Sing dollar may then result in a contractionary effect on the Singapore economy. This is because with an appreciation of the currency, the price of exports increases in foreign currency while the price of imports decreases in local currency. This leads to a fall in the quantity demanded of exports and a rise in the quantity demanded of imports. Assuming the Marshall Lerner condition, net export earnings (X-M) fall overall. This leads to a multiplied decrease in national income, which reduces actual growth and increases cyclical unemployment. As mentioned earlier, the effects on the Singapore economy will be significant given its reliance on exports for growth.

In conclusion, the recent macroeconomic problems faced by Singapore are not a direct result of the weaker Sing dollar. However, it is undeniable that efforts to slow down the appreciation of the Sing dollar or the slight depreciation undertaken in 2009 in order to boost net exports have contributed to higher imported inflation in Singapore. In addition, the recent appreciation of the Sing dollar arising from quantitative easing in the US has also contributed to other macroeconomic problems for Singapore. Nonetheless, we note that any movement in the value of the Sing dollar is likely to be controlled. This is because Singapore maintains a managed float exchange rate system, where the Sing dollar is managed against a trade-weighted basket of currencies of Singapore’s major trading partners, and is allowed to float freely within a policy band. Hence, if the Sing dollar exceeds the upper or lower bound of the policy band, MAS will intervene by buying or selling foreign currency to influence the value of Sing dollar. This ensures that the value of the Sing dollar remains within the policy band, and any impact on export price-competitiveness or import prices will be limited.

Want to learn how to write excellent essays like this one? Sign up for Mr Koh’s Economics Tuition classes. Mr Koh teaches clarity of concept and more importantly how to apply them into the A levels economics exam. With his classes and economics study materials, you will be sure to achieve the Economics grade you desire.

Example 25-Mark Essay in style of AQA Economics A-level

Below is an example model answer to a 25 mark question in the style of AQA Economics A-level.

To practise your AQA exam technique with 2024 practice questions, click the button below:

Question for model answer

Consider the following question. I have written this question in the style of a 25-mark AQA Economics A-level question for section B:

Taking effect from 1st April 2023, the UK Government has committed to increasing the corporation tax rate from 19% to 25% for companies with profits above £250,000 per year. For firms with profits below £50,000, there is no increase in corporation tax rates. But for firms with profits between £50,000 and £250,000 there will be a smaller increase in corporation tax rates.

(Source: here )

Evaluate the effects on the UK economy of increasing corporation tax rates on firms making high profits (25 marks) .

This is a key macroeconomics essay on current affairs. A quick essay plan is here:

Define key terms.
Laffer curve – higher government revenue.
Evaluation – position on Laffer curve.
AD effect – lower investment and negative multiplier effect.
Evaluation – proportion of AD that comes from investment.
LRAS effect – reduced incentives and productivity.
Evaluation – need to compare corporation tax rates to those of other economies.

Conclusion.

Possible model answer

Corporation tax is a tax on firms’ profits. Aggregate demand (AD) is the total demand in the economy, AD = C+I+G+X-M.

Increasing corporation tax rates may increase tax revenue for the UK Government. The Laffer curve shows this. An increase in tax rates from T to T1 raises tax revenue from R to R1. This revenue could go towards reducing the budget deficit. The government’s budget deficit is very high at 4.2% of the UK’s GDP in 2023-24. This is because of government spending on Covid support programmes such as the furlough scheme, energy subsidies and other tax cuts. Reducing the budget deficit may lead to government borrowing and hence reduced debt interest payments . With less spending on debt interest the UK Government could choose to spend more money in the future on other priorities such as healthcare spending. Sounder public finances might also make investors more confident in the UK Government’s ability to make bond repayments. This may reduce the interest rates at which investors are willing to buy government bonds and thus reduce future borrowing costs.

Whether the tax increase raises government revenue depends on the position of the UK economy on the Laffer curve. If instead the economy is at point (T1,R1) before raising corporation taxes, then increasing the corporation tax rate to T2 may decrease revenue to R. This is because a corporation tax rise may reduce incentives to start or grow a business, reducing the size of the tax base. The UK Government does predict that tax revenue would rise by over £10bn a year because of the corporation tax rate rise. With corporate tax rates relatively low now, it is likely that there will be higher revenue. But the effect of higher taxes on incentives may reduce the extent to which revenue increases.

Increasing corporation tax rates “from 19% to 25% for companies with profits above £250,000 per year” reduces the post-tax profits of these firms. This leaves reduced funds for investment, so investment may fall. Also as firms know any future profits will be taxed at a higher rate, this will disincentivise investment further. This is because firms will have reduced returns (lower post-tax profits) from any new investment. So investment falls and as investment is a component of aggregate demand (AD=C+I+G+X-M), aggregate demand shifts left from AD to AD1. This may cause a negative multiplier effect . This is where a fall in investment leads to a larger than proportionate fall in A. Lower investment results in lower incomes for firms and cuts in wages, so consumers cut their spending, meaning consumption also falls and so on. So AD shifts further left to AD2. This results in lower real GDP as real GDP falls from Y to Y2. Hence corporation tax may lower real GDP, likely resulting in lower living standards.

However this argument depends on the proportion of AD influenced by the corporation tax rate rise. Only firms making larger profits are facing a corporation tax rise. So firms making lower profits, for example small businesses, are less likely to reduce their investment. Also consumption is the largest component of AD, making up roughly 60% of AD, not investment. So a given percentage fall in investment may have only a smaller effect on AD. Corporation tax is likely to reduce AD leading to lower real GDP. But these impacts are limited by the relative importance of investment to AD and the design of the policy to target high profit firms only.

Decreased investment can also influence the supply side of the economy. Lower investment could mean reduced firm spending on capital goods and human capital. So this could reduce productivity and hence the productive capacity of the economy. This means the LRAS could shift to the left. Higher corporation taxes could mean higher business costs, shifting the short-run aggregate supply curve left too. Lower productivity and higher business costs could lead to a higher price level in the UK economy, reducing the price competitiveness of UK exports which may widen the current account deficit. Productivity for the UK economy is 15% below the average of other G7 economies (as of 2015), so corporate tax rises could further worsen this UK productivity gap with other nations. There may also be fewer businesses choosing to set up in the UK, preferring to set up abroad in locations with lower taxes. This would further reduce the productive potential of the economy, compared to a situation of lower corporation tax rates.

However this depends on the level of corporation tax rates in other economies . The UK has the lowest corporate tax rate among the G7 economies, even after the tax rise. Hence there may be fewer incentives to set up a business abroad, so the effect on competitiveness is reduced. Also many economies have agreed to a global minimum corporation tax of 15%, further reducing the risk to competitiveness from raising corporation taxes. While the corporation tax rate rise may reduce investment, it is less likely to have a significant impact on competitiveness.

Overall raising corporation tax on firms making high profits is likely to be effective in raising revenue. While raising corporation tax will reduce aggregate demand and aggregate supply, by raising taxes only on higher-profit firms, the impact is limited. The impact of the tax rise does depend on how other countries respond – if other countries maintain or reduce their tax rates to attract more businesses, then increasing corporate taxes could significantly reduce the incentive for international businesses to set up in the UK. However, given the increasing degree of tax cooperation globally , as shown by the 15% minimum corporate tax rate agreement, it seems likely that countries will not seek to undercut each other’s corporate tax rates.

Application is throughout using examples from the short extract and from own knowledge to support analysis and evaluation.

Analysis is detailed, using chains of reasoning and graphs to support the answer.

Evaluation is also detailed, making use of chains of reasoning and where relevant, data about the economy. The conclusion addresses the question and justifies the answer.

Note for the conclusion you could have picked another side for this policy too depending on the arguments used. You could also use other possible points – there is no right way of doing this. For example with interest rates at historical lows, how does that impact the cost of government borrowing and the necessity of raising taxes? What other factors may matter for investment beside corporate tax rates?

This essay would likely score level 5 according to AQA Economics A-level criteria.

For more guidance on AQA exam technique (25 markers, 15 markers, 9 markers and more), check out the blue button below:

How should you structure a 25 marker economics essay?

Introduction

Depending on depth of your previous points, add another round of analysis and evaluation.

For more information on AQA Economics essay structure, I recommend the following article linked here .

How should you write a conclusion for 25 markers economics?

A conclusion has these key elements:

Answer the question.
Justify your answer in step 1.
Consider other evaluation points, including real-world context, for further justification or that may go against your answer.

How should you evaluate in economics 25 markers?

I recommend the “depends on” structure for AQA Economics style evaluation. For more information on this, see my AQA Economics style evaluation guide here .

More Resources

For AQA style practice questions on recent current affairs, click the blue button below:

If you are interested in more A-level Economics resources, please feel free to click the button below:

For A Level Economics tuition for AQA students, see the link below:

About the author

Helping economics students online since 2015. Previously an economist, I now provide economics resources on tfurber.com and tutor A Level Economics students. Read more about me here .

Latest Posts

Edexcel Economics notes 2.6.2 Part 1 – monetary policy
2.6.3 Supply side policies – Edexcel Economics notes
2.2 Aggregate demand notes for Edexcel A Economics
2.1.2 Inflation Edexcel A Economics Notes
Don’t be fooled! How to understand economic statistics

Children's Books
Education & Reference

Sorry, there was a problem.

Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required .

Read instantly on your browser with Kindle for Web.

Using your mobile phone camera - scan the code below and download the Kindle app.

Image Unavailable

To view this video download Flash Player

Economics: Model Essays Paperback – January 30, 2020

Print length 208 pages
Language English
Publisher Sunway University Press
Publication date January 30, 2020
Dimensions 8.07 x 0.4 x 10.04 inches
ISBN-10 9675492031
ISBN-13 978-9675492037
See all details

Editorial Reviews

About the author, product details.

Publisher ‏ : ‎ Sunway University Press (January 30, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 208 pages
ISBN-10 ‏ : ‎ 9675492031
ISBN-13 ‏ : ‎ 978-9675492037
Item Weight ‏ : ‎ 2.31 pounds
Dimensions ‏ : ‎ 8.07 x 0.4 x 10.04 inches
#2,430 in Children's Money & Saving Reference (Books)

Customer reviews

5 star 4 star 3 star 2 star 1 star 5 star 0% 0% 0% 0% 0% 0%
5 star 4 star 3 star 2 star 1 star 4 star 0% 0% 0% 0% 0% 0%
5 star 4 star 3 star 2 star 1 star 3 star 0% 0% 0% 0% 0% 0%
5 star 4 star 3 star 2 star 1 star 2 star 0% 0% 0% 0% 0% 0%
5 star 4 star 3 star 2 star 1 star 1 star 0% 0% 0% 0% 0% 0%

Customer Reviews, including Product Star Ratings help customers to learn more about the product and decide whether it is the right product for them.

To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzed reviews to verify trustworthiness.

No customer reviews

About Amazon
Investor Relations
Amazon Devices
Amazon Science
Sell products on Amazon
Sell on Amazon Business
Sell apps on Amazon
Become an Affiliate
Advertise Your Products
Self-Publish with Us
Host an Amazon Hub
› See More Make Money with Us
Amazon Business Card
Shop with Points
Reload Your Balance
Amazon Currency Converter
Amazon and COVID-19
Your Account
Your Orders
Shipping Rates & Policies
Returns & Replacements
Manage Your Content and Devices

Conditions of Use
Privacy Notice
Consumer Health Data Privacy Disclosure
Your Ads Privacy Choices

How to Write a Good Economics Essay

Governor November 28, 2019 Real World Applications 3 Comments

Many students ask “How to write an economics essay?” This Guide to Writing a Good Economics Essay is applicable to both IB economics as well as the Singapore JC A-Level H2 economics examinations. Many of the pointers here are also applicable to large-mark case study questions.

6 Steps to Writing a Good Economics Essay

Step 1: dissect the question.

Make sure you analyse and fully understand the KEYWORDS and REQUIREMENTS of the question. This is a very important skill that is taught in our economics tuition classes .

For example, “Best”, “Most Effective” are closely related but mean different things.

Paraphrase the question to make it simpler if necessary.

Take note of the command word (eg: Explain, Discuss) as it determines the approach needed for the essay, for example, whether two sides are needed or one side is sufficient. Below are some common examples found in economics essay questions:

Command Words Action Required

Account for Explain why

Analyse Break it down into step-by-step explanations

Assess For & Against. Consider other factors.

Compare Identify Similarities & Differences

Distinguish Point out differences

Discuss Explore both sides

Evaluate The Good and The Bad.

Explain Show why and how

Explain whether Cover both possibilities

Examine Look closely. How so and how not so?

To What Extent Yes…..But….Judgment

Remember to look out for the context in the question. This is usually given in the form of a country (eg: Singapore). The examples in your essay must be tailored to this particular context (for example, do not suggest interest rate policy for Singapore as that is considered infeasible in the Singapore context). If no context is given, any real-world example can be used.

Keep in mind the question throughout the essay and remember to always answer the question. Don’t go off-point!

Common Examiner’s Comment : Not Answering Question (NAQ))

Step 2: Plan Your Answer

Take some time to consider what economic framework you will use to approach the question. Scribble down your main thesis and anti-thesis points. Ensure they ANSWER THE QUESTION.

Step 3: Essay Introduction

In the introduction, include definitions of keywords in the question and spell out the economic framework you will employ for your answer as well as key definitions.

Step 4: Body of Essay

In the body , there will be several paragraphs.

The number of points/paragraphs depends on the question. It is common to require 2 main points for each 10 mark essay and similarly for 15 mark essay questions. Under each main point, there may be 1-2 sub-points.

Use one paragraph for each sub-point you are making.

However, do not be too focussed on the number of points or paragraphs. The key is to answer the question.

For each body paragraph , use TET’s PEEL(ED) structure. Include only one main idea per paragraph.

Point – Write your point in the first sentence so that markers will know what the paragraph will be about. The topic sentence must directly answer the question!
Explanation – Explain what you mean
Elaboration – Provide further analysis with clear step-by-step economic reasoning. This part may be done with examples as well as diagrams.
Link – Link your explanations back to the Point and to answer the question.
Exemplification – Give an example to support your reasoning. It can be statistics or real-world examples (for Case Studies, evidences from the Case must be uncovered!)
Diagram – Where possible, araw an appropriate diagram with correct labelling and refer to it in your answer. This is crucial to show economic reasoning. Diagrams are very important for economics essays!

These are of course much easier said than done! Thus, students in our economics tuition classes are regularly honed to achieve such output including with tips and tricks to spark off the correct thinking process.

Our resources including the Study Guides for A Level and IB economics also provide a very powerful and handy reference on the depth of analysis required to score the highest marks.

Common Examiner’s Comment : Mere statements and claims. No economic rigour.

Step 5: In-Body Evaluation

This applies especially to the 15 mark essays for A-Level Economics. A total of 5 marks is catered for Evaluation. Students should attempt to achieve about 2-3 in-body evaluation marks by pointing out how the thesis and anti-thesis points may not be true due to certain assumptions made that may not hold. Students may write “However,….may not necessarily happen……It would depend on whether….”. This statement can be written after the associated sub-point has been made.

Step 6: CONCLUDING SECTION

This only applies to the 15 mark essay questions.

Earn more evaluation marks by making a reasoned judgement. Deliver your verdict like a Judge!

Check back on the question before you embark on this. Ensure your judgement answers the question.

So the question now is, how does a judge arrive at and deliver a verdict? Certainly, you should not be summarising or merely paraphrasing your main points in the conclusion. Obviously, you cannot expect more marks by saying the same thing over and over again!

After a verdict and reasons have been provided, consider providing further relevant insights and/or recommendations.

Common Examiner’s Comment : Repetitive. Mere Summary.

Here are some quite common types of Concluding Sections

Consider the relative importance of thesis and anti-thesis factors. Which factors are most important or pertinent in the given context? For example, certain policies better fit specifc types of economies.
Consider short-term vs long-term pros and cons. Do the short-term benefits outweigh the long-term costs? Is the policy more effective in the long-term, and if so, how pressing is the problem that needs to be addressed?
Suggest a multi-policy approach, in which each policy has strengths and weaknesses that allow them to complement each other.

There is no way to really memorise evaluation points as every question and context is different. After all, you are being tested on higher-order thinking!

There are other evaluation tips that our students will receive but the key point here is that the training of the mind to think and apply economics is essential. That is where our weekly economics lessons come into play and that is why our students are often asked questions in class and trained to think on their feet. As ex-student Xue Min from YIJC testified, Chief Tutor Mr. Kelvin Hong does not just spoon-feeds our students but mentors them in their thinking to arrive at the answers. This was different from other tutors that her classmates experienced and eventually this was the key to Xue Min’s A grade.

In your essay, write in simple and clear sentences. Everything you write should be value-adding. You do not have to spend time showing off vocabulary as no extra points are awarded for language. Focus on economic reasoning. Use succinct and effective examples which support the point you are trying to make as well as accurate diagrammatic analyses.

For samples of great economics essays, please check out our free Economics Model Essays and sample Past JC A-Level Economics Questions and Answers .

For our econs publications that are sold worldwide, please check out our A Level & IB Economics Study Guides and Model Essays Publications

About The Economics Tutor

Founded by Kelvin Hong in 1998, The Economics Tutor is one of the leading economics tuition in Singapore . We provide a comprehensive program to guide students in understanding complex economic concepts and applying them through case study analyses, essay writing and discussion of real world events.

For 24 years, the way we teach JC Economics Tuition (A Level Economics Tuition) and IB Economics Tuition classes helped learners appreciate economics and everything it entails on a much larger scale. We take things step-by-step, implement effective techniques in memorising frameworks and give every student the chance to nurture their ideas.

We don’t just solely focus on helping you get stellar grades and perfect scores. We make sure that we also hone the critical thinking skills and investment / business decisions you can use outside the four walls of your classroom.

Looking for a fun, engaging and probably the best economics tutor in Singapore? Look no further—check out our extensive and high quality economics resources on the website such as our IB and A Level Economics Publications

Book your lesson today and master the nuances of economics in our next class!

its good knowledgeable post regarding ib economics commentaries. i just wanted to admin can i use your blog as reference to my students .

Go ahead. We are all for helping students learn economics well.

Thank you, A lot of info!

To submit your Book proposal, please click here

Record : found
Abstract : found
Book : found

Economics : Model Essays

Read this book at

Review book
Invite someone to review

Economics: Model Essays is tailored to prepare students for Paper 2 and Paper 4 of the 2019-2021 Cambridge International AS & A Level Economics (9708) examination. The model essays are specially written as answers to selected past year questions, and are supported with essay outlines and notes on how students can successfully compose convincing essays.

Related collections

Sunway University Press

Author and book information

Comment on this book, similar content 362 .

Linfoma do sistema nervoso central: ensaio iconográfico Translated title: Central nervous system lymphoma: iconographic essay Authors: Fabiano Reis , Ricardo Schwingel , Felipe Barjud Pereira do Nascimento
Perceptions of writing prowess in honours economic students Authors: Jean du Toit , Anmar Pretorius , Henk Louw …
Influence of the physical growth indicators of primary and middle school students on hemoglobin in different economic zones of Ningxia Authors: MA Ling , ZHANG Wenxia , ZHANG Hui …

Free Economics Essay Samples

How to Write a Good H1 or H2 Economics Essays for Your GCE ‘A’ Level Paper?

Would you like to have quality ideas on how to write good quality answers for your Economics essays?

If you believe in modeling good sample essays and case studies for self learning, you have come to the right place!

(Yup, in preparing for the actual GCE A Level Econs Exams, we use our prelim papers from the local junior colleges, as well as very recent past year official ULCES and MOE exam papers.)

All our individual tuition and small group tuition students are given FREE Instant Access to Question Papers & Answers for Economics Essays & Case Studies.

And we have resources from all the junior colleges, including the preliminary examination papers, and the actual GCE ‘A’ Level Economics questions, plus essay outlines, case study answers and full length answers that our very own tutors have prepared.

If you would like to have free access to our database of questions and answers for essay and case studies, request for your access now at 1800-TUITION!

For a quick demo, kindly look the the following 2 sample answers here:

1. Microeconomics Essay Sample Answer

2. Macroeconomics Essay Sample Answer

Common Mistakes Students make in Economics Essay Writing

Many students do revise and study the lecture notes, and attempt to do their essay writing. However, this approach leads itself into several common mistakes, namely:

1) Write EXCESSIVELY on the basic content answers. This happens because students very often have no idea what to write, so they regurgitate largely from the recall of the lecture notes.

2) Students did not realise that the moment they recall the microeconomic objective of the government, they will never be guilty of going out of point and suffer from irrelevance anymore. (Note that they are exceptions to this rule of thumb).

3) Despite numerous written attempts, students still usually do not know how to interpret essay questions, and thus how to write what the question is looking for.

We at Adam Smith Economics Tuition Agency, impart our proven 5 step approach to answering any essay questions, no matter if it is for H1 or H2 Micro or macro Economics.

Depending on your preferences, you can ease your Economics exam worries by taking our group tuition programme . Alternatively, you can receive undivided attention at your own home with our customised private tuition services.

(Back to Economics Tuition SG – Resources )

Free Trial Lessons

Click to view our JC Econs Video Lessons

100% F.R.E.E. Ultimate Guide To Scoring For Your CSQ

AS Economics Model Essays

£ 8.00

A selection of 40 AS Level model economic essays
Comprehensive answers which illustrate – how to answer the question, include sufficient evaluation and get the top A grade.
E-book (pdf) sent within a couple of hours after purchase.
I have chosen questions which are appropriate for all exam boards – AQA, Edexcel, OCR, WJEC.
Suitable for new 2016 AS syllabus
The model answers are comprehensive and written to a high grade A standard.
For Network license (unlimited use in schools) – £70.00

Description

Model essays will help.

Essay writing technique.
How to answer the question asked.
How to evaluate questions which require it.
Separate page giving advice on the important evaluation component of the exam.
The essays were all written exclusively for this pdf guide.
List of 41 questions included in the pdf at the bottom.

The cost for AS Level Essays is only £8.00

View: sample of AS essays

Using the Model Essays

A good way to use this guide is to take a question and write down an essay plan of what you would write for the question. Then have a look at my answer, see what is missing, consider whether you understood what the question was asking.
Also, note how much evaluation I will add for any question which asks for Discuss, Evaluate, Assess e.t.c
There are other points that could be made in these essays, there are often different ways that you can get full marks. But, one feature of these essays is that they don’t waste any words writing about issues not relevant to the question.
How Long Should Essays Be? These essays are not necessarily long because I only include information relevant to answering the question. It is beneficial to learn how to write specifically to answer the question.

I have marked 1,000s of A level exam papers during the past 6 years I have worked for Edexcel. Being an examiner gives a very good idea of what is necessary to get full marks.

List of AS Micro Essays in Guide

Evaluate the case for and against governments intervening to try to stabilise the price of copper, for example, through setting up a buffer stock scheme.
Evaluate advantages and disadvantages of various methods of government intervention to correct market failure arising from aircraft emissions.
Starbucks, Cafe Nero and others have seen rapid expansion in the UK. Discuss the likely effects on the retail market for coffee if there is a large increase in city centre rents.
In the UK, students face increasing tuition fees. Discuss the benefits and costs to society of abolishing all tuition fees.
Discuss three policies to reduce the level of cigarette smoking amongst under 21s.
Discuss the extent to which governments should subsidise companies who are developing cars which run on clean fuels such as hydrogen?
Discuss whether the government is mistaken to worry about monopoly power?
Discuss the advantages and disadvantages of the government intervening in agricultural markets?
Discuss the effects on UK business of a rise in fuel prices.
Discuss whether the government should end free health care for people and make them take out private health care insurance like in the US?
Discuss the role that pollution permits could play in reducing global warming.
Discuss the case for implementing a congestion charge for driving into Birmingham city centre.
Discuss the micro economic effects of finding and exploiting extensive oil reserves in the Antarctic.
Discuss whether governments should subsidise food prices.
Assess whether environmental problems caused by rubbish disposal can best be dealt with by market forces or government intervention.
Explain why poor housing conditions and homelessness can lead to negative externalities and evaluate the role that governments could play in avoiding market failure in the housing market.
Evaluate the view that the provision and maintenance of flood defences should be paid for solely by the government.
Discuss whether the government should subsidise food prices?
Discuss whether a minimum price for alcohol would help to reduce market failure in this particular market.
Evaluate the impact of a guaranteed minimum price in the beef market on consumers and producers. Use an appropriate diagram in your answer.

List of Macro Economic Essays

Evaluate government policies which might improve the UK balance of trade in goods and services.
Evaluate the importance of rising productivity in bringing about an improvement in the UK balance of payments on current account.
Discuss the role of supply side policies in improving rates of economic growth.
Evaluate the impact of a slowdown in the US on the UK economy.
Discuss how a government might try to deal with economic shocks.
Discuss the impact of a rise in the savings ratio on the UK economy.
Evaluate the importance of managing aggregate demand to bring about a sustained reduction in the rate of unemployment in the UK economy.
Evaluate the effect of a fall in house prices on the UK macro economy.
Discuss whether government policies can overcome a recession.
Discuss the economic effects of an increase in the inflation rate.
Discuss the impact of an increase in government expenditure on the UK economic performance.
Discuss the impact on the EU economy of a rise in the value of the Euro against its main trading partners
Discuss the relative merits of interest rates and tax increases as a means to control inflation in the UK.
Discuss the effects of a rise in exports on UK aggregate demand.
Discuss the extent to which shifting resources into the public sector will help the government achieve its macro economic objectives.
Discuss the effectiveness of a cut in the rate of interest in influencing consumption and investment
Discuss the impact of an increase in inward investment into the UK economy.
Evaluate the importance of supply-side policies in achieving the objectives of low inflation and low unemployment.
Discuss the effectiveness of monetary policy in dealing with a period of inflation.
Assess the contribution that fiscal and monetary policies can make in maintaining a stable economy.

Related products

OCR A level economics revision guide

Exam Tips for Economics

OCR GCSE Economics Revision Guide

WJEC AS economics revision guide

International
Education Jobs
Schools directory
Resources Education Jobs Schools directory News Search

A-Level Economics Theme 4 model essays (A* level) (Suitable for 2024 exams)

Subject: Economics

Age range: 16+

Resource type: Assessment and revision

Last updated

Share through email
Share through twitter
Share through linkedin
Share through facebook
Share through pinterest

Want to ace A-Level Economics Theme 4 Macroeconomics? Then these A* level model essays are perfect for you!

When it comes down to writing essays for A-Level Economics it can get a bit tricky. This resource is packed with helpful guidance for you to succeed, ranging from model essays on key topic areas to structures for a top essay.

I did all of this and in the 2022 exams I achieved 88% (295/335) in my actual A-Level, a comfortable A* by over 10%!

*NB: This resource has been designed for Edexcel, however, it is suitable for all exam boards due to the overlap in the content.

Tes paid licence How can I reuse this?

Get this resource as part of a bundle and save up to 14%

A bundle is a package of resources grouped together to teach a particular topic, or a series of lessons, in one place.

A-Level Economics A* model essays for All Themes (Suitable for 2024 exams)

Want to ace A-Level Economics? Then these A* level model essays are perfect for you! When it comes down to writing essays for A-Level Economics it can get a bit tricky. This resource is packed with helpful guidance for you to succeed, ranging from model essays on key topic areas to structures for a top essay. I did all of this and in the 2022 exams I achieved 88% (295/335) in my actual A-Level, a comfortable A* by over 10%! *NB: This resource has been designed for Edexcel, however, it is suitable for all exam boards due to the overlap in the content.

Your rating is required to reflect your happiness.

It's good to leave some feedback.

Something went wrong, please try again later.

This resource hasn't been reviewed yet

To ensure quality for our reviews, only customers who have purchased this resource can review it

Report this resource to let us know if it violates our terms and conditions. Our customer service team will review your report and will be in touch.

Not quite what you were looking for? Search by keyword to find the right resource:

Get the Reddit app

r/alevel is a subreddit for A Level students and aspirants. It is a place to ask, share, and learn about any A Level subject, exam, or plan. You can also find resources, memes, and friends on r/alevel. Join and have fun!

9708 AS Economics F/M/2023 Model Essay 2(a)

Download All As app to write Economics Essay

https://apps.apple.com/app/apple-store/id1645087928?pt=125545751&ct=reddit&mt=8

Want to ace your A-Level economics exams? We've compiled perfect model essays and formulas to help you score big! 💯⭐️

Gain insider tips to make your essays stand out. 💡✍️

Don't hesitate, master these precious exam secrets now!

go appstore i n iphone or googleplay to download All As.

By continuing, you agree to our User Agreement and acknowledge that you understand the Privacy Policy .

Enter the 6-digit code from your authenticator app

You’ve set up two-factor authentication for this account.

Enter a 6-digit backup code

Create your username and password.

Reddit is anonymous, so your username is what you’ll go by here. Choose wisely—because once you get a name, you can’t change it.

Reset your password

Enter your email address or username and we’ll send you a link to reset your password

Check your inbox

An email with a link to reset your password was sent to the email address associated with your account

Choose a Reddit account to continue

Reference Library

Collections

See what's new
All Resources
Student Resources
Assessment Resources
Teaching Resources
CPD Courses
Livestreams

Study notes, videos, interactive activities and more!

Economics news, insights and enrichment

Currated collections of free resources

Browse resources by topic

All Economics Resources

Resource Selections

Currated lists of resources

Exam Support

Edexcel Economics (A) A Level: 2024 Microeconomics Topic Tracker

Last updated 2 Sept 2024

Share on Facebook
Share on Twitter
Share by Email

Here's our latest Edexcel Economics (A) A level microeconomics topic tracker, fully updated with the summer 2024 exams.

Download your free resource.

Confidence in handling economic data in exam papers, answering the 12 mark edexcel as macro question, terms of trade (chain of analysis), competition and consumer welfare (essay technique video), essay on oligopoly and collusion, key topics to revise for paper 1 micro in 2019, edexcel paper 3: synoptic essay on a currency depreciation, edexcel a-level economics (a) theme 2 knowledge organiser pack 2, our subjects.

› Criminology
› Economics
› Geography
› Health & Social Care
› Psychology
› Sociology
› Teaching & learning resources
› Student revision workshops
› Online student courses
› CPD for teachers
› Livestreams
› Teaching jobs

Boston House, 214 High Street, Boston Spa, West Yorkshire, LS23 6AD Tel: 01937 848885

› Contact us
› Terms of use
› Privacy & cookies

¿En qué podemos ayudarle?

Co n gresos

Three essays on enviromental and economic sustainability of fisheries

Autores: Alberto Roca Florido
Directores de la Tesis: Emilio Padilla ( dir. tes. )
Lectura: En la Universitat Autònoma de Barcelona ( España ) en 2022
Idioma: inglés
Tribunal Calificador de la Tesis: Jordi Roca Jusmet ( presid. ), Vicent Alcántara Escolano ( secret. ), Andreas Tsakiridis ( voc. )
Programa de doctorado: Programa de Doctorado en Economía Aplicada por la Universidad Autónoma de Barcelona
Economía general
Tesis en acceso abierto en: TDX

La tesis pretende contribuir a la consecución, de forma equilibrada, de los objetivos de sostenibilidad económica de la industria pesquera y sostenibilidad ambiental de los recursos marinos. La tesis contiene tres trabajos empíricos para dar respuesta al objetivo de la UE de erradicar la sobrepesca. El primer capítulo analiza el problema de la sobrepesca en el contexto de los desajustes entre los montos de las cuotas de pesca y los montos sugeridos por los científicos del ICES. El no considerar las recomendaciones científicas ha favorecido la reducción de la biomasa de algunos stocks a niveles alarmantes, teniendo importantes implicaciones a nivel biológico, económico y social. El análisis de este capítulo de la tesis se centra en seis de estos stocks que representan el 85% del total de las cuotas de pesca establecidas por encima de las recomendaciones ICES. A través de un panel de 10 países y 14 años y un modelo teórico basado en una función de “producción” pesquera, analiza qué factores socioeconómicos, además de dicho desajuste, han contribuido a la degradación de la biomasa de estos seis stocks. En un segundo análisis, utilizando un modelo de panel, estimamos la probabilidad de que las capturas de los seis peces analizados se ajusten a lo que recomiendan los científicos cuando cada uno de los factores se incrementa en un 10%. Los resultados del primer análisis nos permiten hacer recomendaciones de política a incluir en las próximas reformas de la PPC, mientras que tener en cuenta la magnitud de la probabilidad en el segundo análisis ayuda a identificar por cuál de estos factores se deben priorizar las políticas. Los capítulos 2 y 3 se centran en analizar las consecuencias para la economía española de una reforma que elimina por completo las subvenciones perjudiciales. Este es uno de los objetivos incluidos en la futura estrategia europea Horizonte 2030 y sería interesante saber cómo se comportaría la economía si ya estuvieran eliminados del programa de fondos europeos marinos y de pesca (FEMP) para 2021-2027. Para realizar este análisis, tomamos como contexto el anterior programa de fondos FEMP para 2013-2020, que fue diseñado para la pasada estrategia Horizonte 2020. El análisis se centra en España, ya que era entonces el principal receptor de los fondos entre los países de la UE, recibiendo aproximadamente el 20% del presupuesto total. En este contexto, el Capítulo 2 realiza una primera aproximación al análisis de los potenciales impactos de esta reforma a nivel interindustrial sobre el valor añadido bruto; los efectos de atracción entre industrias; y los impactos en la demanda final. El análisis se realiza con un modelo input-output, se simulan cinco escenarios diferentes. El análisis se centra en tres sectores; pesca extractiva, acuicultura y procesamiento de productos del mar. La desagregación de dos de los tres sectores (pesca extractiva y acuicultura) en las tablas input-output de España ha permitido un análisis más detallado. El tercer capítulo complementa el análisis realizado en el segundo capítulo. Se centra en el flujo completo de transacciones que se producirían en la economía española, desde los sectores industriales, las demandas de productos para la producción entre sectores, la renta familiar y las exportaciones, hasta los impactos en el empleo por el choque exterior provocado por la eliminación de las perjudiciales subvenciones a la pesca. El análisis se realiza a través del método de una Matriz de Contabilidad Social (SAM).En este tercer capítulo se simulan 8 escenarios diferentes, en los que en los primeros cuatro se considera únicamente la eliminación de subsidios, mientras que en los últimos cuatro se contempla la posibilidad de reasignarlos a otro sector de I+D+i.

The thesis aims to contribute to the achievement, in a balanced way, of the objectives of economic sustainability of the fishing industry and environmental sustainability of marine resources. The thesis contains three empirical works to respond to the EU objective of eradicating overfishing. The first chapter analyses the problem of overfishing in the context of the mismatches between the amounts of fishing quotas and the amounts suggested by ICES scientists. Not considering scientific recommendations has favoured the reduction of the biomass of some stocks to alarming levels, having important implications at biological, economic and social levels. The analysis of this chapter of the thesis focuses on six of these stocks that represent 85% of the total fishing quotas set above the ICES recommendations. Through a panel of 10 countries and 14 years and a theoretical model based on a fishing "production" function, it analyses which socioeconomic factors, in addition to said mismatch, have contributed to the degradation of the biomass of these six stocks. In a second analysis, using a panel model, we estimate the probability that the catches of the six analysed fish conform to what the scientists recommend when each of the factors is increased by 10%. The results of the first analysis allow us to make policy recommendations to be included in the next reforms of the CFP, while taking into account the magnitude of the probability in the second analysis helps to identify for which of these factors should policies be prioritised. Chapters 2 and 3 focus on analysing the consequences for the Spanish economy of a reform that completely eliminates harmful subsidies. This is one of the objectives included in the future European Horizon 2030 strategy and it would be interesting to know how the economy would behave if they were already eliminated from the European marine and fisheries funds program (EMFF) for 2021-2027. To carry out this analysis, we take as a context the previous program of EMFF funds for 2013-2020, which was designed for the past Horizon 2020 strategy. The analysis focuses on Spain, since it was then the main recipient of the funds among the countries of the EU, receiving approximately 20% of the total budget. In this context, Chapter 2 makes a first approach to the analysis of the potential impacts of this reform at the interindustrial level on gross added value; the pulling effects between industries; and the impacts on final demand. The analysis is carried out with an input-output model, five different scenarios are simulated. The analysis focuses on three sectors; extractive fishing, aquaculture and seafood processing. The disaggregation of two of the three sectors (extractive fishing and aquaculture) in the input-output tables of Spain has allowed a more detailed analysis. The análisis is novel as it is one of the first studies to address this type of study. The third chapter complements the analysis carried out in the second chapter. It focuses on the complete flow of transactions that would occur in the Spanish economy, from industrial sectors, product demands for production between sectors, family income and exports, to the impacts on employment due to the external shock caused by the elimination of fishing harmful subsidies. The analysis is carried out through the method of a Social Accounting Matrix (SAM). In this third chapter, 8 different scenarios are simulated, in which, in the first four, only the elimination of subsidies is considered, while in the last four, the possibility of reassigning them to another sector that can contribute to mitigating the impacts is contemplated, as can be the case of the R&D sector.

El primer capítol analitza el problema de la sobrepesca en el context de les diferències entre les quantitats de quotes pesqueres i les quantitats suggerides pels científics del ICES. No considerar les recomanacions científiques ha afavorit la reducció de la biomassa d’algunes poblacions a nivells alarmants, tenint importants implicacions en els nivells biològic, econòmic i social. L’anàlisi d’aquest capítol de la tesi se centra en sis d’aquestes poblacions, que representen el 85% del total de quotes pesqueres establertes per sobre de les recomanacions del ICES. A través d’un grup de 10 països i 14 anys i d’un model teòric basat en una funció de “producció” pesquera, analitza quins factors socioeconòmics, a més d’aquest desajustament, han contribuït a la degradació de la biomassa d’aquestes sis poblacions. En una segona anàlisi, utilitzant un model de panell, estimem la probabilitat que les captures dels sis stocks analitzats s’ajustin al que els científics recomanen quan cada un dels factors s’incrementi en un 10%. Els resultats de la primera anàlisi ens permeten fer recomanacions polítiques que s’incloguin en les pròximes reformes de la PPC, tenint en compte la magnitud de la probabilitat en la segona anàlisi, ajuda a identificar per a quins d’aquests factors s’hauria de donar prioritat a les polítiques. Els capítols 2 i 3 se centren en l’anàlisi de les conseqüències per a l’economia espanyola de la reforma que elimina per complet les subvencions perjudicials. Aquest és un dels objectius inclosos en la futura estratègia europea per a l’horitzó 2030 i seria interessant saber com es comportaria l’economia si ja s’eliminessin del programa europeu de fons marins i pesquers (MEM) per a 2021-2027. Per a dur a terme aquesta anàlisi, prenem com a context l’anterior programa de fons EMFF per a 2013-2020, que va ser dissenyat per a l’anterior Estratègia Horizon 2020. L’anàlisi se centra en Espanya, ja que llavors va ser el principal receptor dels fons entre els països de la UE, rebent aproximadament el 20% del pressupost total. En aquest context, el capítol 2 planteja un primer enfocament de l’anàlisi dels possibles impactes d’aquesta reforma a escala interindustrial sobre el valor afegit brut; els efectes de la tensió entre les indústries i els impactes sobre la demanda final. L’anàlisi es duu a terme amb un model d’entrada-sortida, se simula cinc escenaris diferents. L’anàlisi se centra en tres sectors: la pesca extractiva, l’aqüicultura i el processament de la marisc. El tercer capítol complementa l’anàlisi realitzada en el segon capítol i es centra en el flux complet de les transaccions que es produirien en l’economia espanyola, des dels sectors industrials, les demandes de producció entre sectors, els ingressos familiars i les exportacions, fins als impactes sobre l’ocupació a causa del xoc extern causat per l’eliminació de les subvencions nocives per a la pesca. L’anàlisi es duu a terme a través del mètode d’una Matrix de Comptabilitat Social (SAM). En aquest tercer capítol,s’han simulat 8 escenaris diferents, en els quals, en els quatre primers, només es considera l’eliminació de les subvencions, mentre que en els últims quatre es contempla la possibilitat de reassignar-les a un altre sector que pugui contribuir a mitigar els impactes, com pot ser el cas del sector I+D+i.

Acceso de usuarios registrados

Dialnet Plus

Opciones de compartir

Opciones de entorno.

Sugerencia / Errata

Accesibilidad
Aviso Legal
R e gistrarse

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
Explore content
About the journal
Publish with us
Sign up for alerts
Open access
Published: 03 September 2024

Evaluating coupling coordination between urban smart performance and low-carbon level in China’s pilot cities with mixed methods

Xiongwei Zhu 1 ,
Dezhi Li 1 , 2 ,
Shenghua Zhou 1 ,
Shiyao Zhu 3 &
Lugang Yu 1

Scientific Reports volume 14 , Article number: 20461 ( 2024 ) Cite this article

Metrics details

Climate-change adaptation
Climate-change impacts
Environmental impact
Sustainability

The construction models of smart cities and low-carbon cities are crucial for advancing global urbanization, enhancing urban governance, and addressing major urban challenges. Despite significant advancements in smart and low-carbon city research, a consensus on their coupling coordination remains elusive. This study employs mixed-method research, combining qualitative and quantitative analyses, to investigate the coupling coordination between urban smart performance (SCP) and low-carbon level (LCL) across 52 typical smart and low-carbon pilot cities in China. Independent evaluation models for SCP and LCL qualitatively assess the current state of smart and low-carbon city construction. Additionally, an Entropy–TOPSIS–Pearson correlation–Coupling coordination degree (ETPC) analysis model quantitatively examines their relationship. The results reveal that smart city initiatives in China significantly outperform low-carbon city development, with notable disparities in SCP and LCL between eastern, non-resource-based, and central cities versus western, resource-dependent, and peripheral cities. A strong positive correlation exists between urban SCP and overall LCL, with significant correlations in management, society, and economy, and moderate to weak correlations in environmental quality and culture. As SCP levels improve, the coupling coordination degree between the urban SCP and LCL systems also increases, driven primarily by economic, management, and societal factors. Conversely, the subsystems of low-carbon culture and environmental quality show poorer integration. Based on these findings, this study proposes an evaluation system for smart and low-carbon coupling coordination development, outlining pathways for future development from the perspective of urban complex systems.

An empirical analysis of the coupling and coordinated development of new urbanization and ecological welfare performance in China’s Chengdu–Chongqing economic circle

Decomposing the comprehensive efficiency of major cities into divisions on governance, ICT and sustainability: network slack-based measure model

Impact of urban spatial structure elements on carbon emissions efficiency in growing megacities: the case of Chengdu

Introduction.

Cities, as centers of population and economy, play crucial roles in cultural exchange, social integration, transportation, communication, and disaster response in modern societal development 1 , 2 . According to the United Nations Human Settlements program’s “2022 World Cities Report”, as of 2021, the global urbanization rate has reached 56%, and it is projected that by 2050, an additional 2.2 billion people will live in cities, increasing the urbanization rate to 68% 3 . North America and European countries are approaching urbanization saturation, with little fluctuation expected, while urbanization in Asia and Africa will accelerate notably 4 . Particularly in China, the world’s second-largest economy, as of 2022, the urbanization rate is only 64.7%, ranking 96th globally, indicating significant potential for growth compared to developed countries like the USA and the UK 5 . The Chinese government places high importance on urbanization development. It was clearly stated in the “2020 State Council Government Work Report” that new urbanization is a key measure for achieving China’s modernization. Moreover, in the “14th Five-Year Plan (2021–2025) and the Long-Range Objectives Through the Year 2035”, detailed strategies are outlined for optimizing the urban layout and promoting urban–rural integration, among other policies to advance urbanization 6 . However, urbanization, as a process of continuous concentration of population and industrial elements in cities, while bringing opportunities for economic growth and social development, also presents a series of challenges such as environmental pressure, resource constraints, and increased demand for services 7 , 8 .

In 2008, the American company IBM introduced the concept of a “Smart Planet”, which garnered widespread attention globally 9 . The concept of a smart city, as a specific application within this framework, aims to enhance urban management and service efficiency through the integration and innovative application of Information and Communication Technology (ICT), thereby improving the quality of life for residents, optimizing resource use, reducing environmental impact, and promoting economic development and social progress 10 , 11 . Currently, the smart city construction model is seen as one of the effective means to advance global urbanization, improve urban governance, and solve major urban issues 12 . In 2009, IBM released the “Smart Planet: Winning in China” plan, outlining China’s five major thematic tasks in constructing a “Smart Planet” (sustainable economic development, corporate competitiveness, energy efficiency, environmental protection, and social harmony) 13 . The construction of smart cities, as a key measure to achieve these thematic tasks, has received significant attention from the Chinese government. In 2014, the Chinese government elevated smart city construction to a “national strategy”, considering it a cornerstone of China’s future economic and urban development strategies. By 2016, over 500 Chinese cities had initiated or announced smart city pilot construction plans, accounting for nearly half of all such projects planned or underway globally 14 . In recent years, with the continuous release of policy benefits related to smart city construction in China and substantial capital investment, China has become a leader in driving global smart city initiatives 15 . However, an undeniable fact is that while smart city construction models promote economic development and improve the quality of life for residents, the new infrastructure supporting the operation of smart cities, such as big data centers, 5G shared base stations, and Beidou ground-based augmentation stations, result in substantial energy consumption and significant carbon emissions 16 . Research shows that in 2018, the total electricity consumption of data centers in China supporting IT infrastructure reached 160.9 billion kilowatt-hours, exceeding the total electricity consumption of Shanghai for that year and accounting for about 2% of China’s total electricity consumption, with carbon emissions nearing 100 million tons 17 . The Environmental Defense Fund (EDF) predicts that by 2035, the total electricity consumption of China’s data centers and 5G base stations will reach 695.1–782 billion kilowatt-hours, accounting for 5–7% of China’s total electricity consumption, with total carbon emissions reaching 230–310 million tons 18 .

In 2022, global energy-related CO 2 emissions increased by 0.9%, reaching a record high of over 36.8 Gt. Concurrently, atmospheric CO 2 concentrations continued to rise, averaging 417.06 parts per million, marking the eleventh consecutive year with an increase exceeding 2 ppm 19 . According to the World Meteorological Organization (WMO), the global surface temperature in September 2023 was 1.44 °C higher than the twentieth century average, setting a new historical record 20 . The continuous rise in global temperatures has led to frequent occurrences of disastrous events such as extreme heat, torrential rains, floods, forest fires, and hurricanes in recent years, causing significant loss of life and property damage 21 . World Health Organization (WHO) data indicates that in 2022, there were at least 29 weather disaster events globally causing billions of dollars in losses, with approximately 61,672 deaths in Europe due to heatwave-related causes 22 . As global climate issues become increasingly severe, the call for global carbon emission reduction is growing louder. Cities, as highly concentrated areas of population and economic activities, according to the Global Report by the United Nations Human Settlements Programme (UN-Habitat), consume 60–80% of the global energy and contribute to over 75% of global CO 2 emissions 23 . As the largest global emitter of carbon, China’s CO 2 emissions in 2022 accounted for 27% of the global total 24 . Given China’s influence in the global economy, technological innovation, and international cooperation, international organizations and global climate policies generally believe that China’s efforts in carbon reduction are crucial to achieving the global 1.5 °C climate goal 25 . In recent years, the Chinese government has actively promoted the construction of low-carbon pilot cities. To date, three batches of low-carbon pilot cities have been implemented in China, bringing the total number of such cities to 81 26 .

However, the report “China’s Digital Infrastructure Decarburization Path: Data Centers and 5G Carbon Reduction Potential and Challenges (2020–2035)” indicates that compared to peak carbon emissions expected around 2025 in key sectors like steel, building materials, and non-ferrous metals in China, the “lock-in effect” of carbon emissions from digital infrastructure poses a significant challenge to achieving China’s peak carbon and carbon neutrality goals 27 , 28 , 29 . Given the urgency of global climate change, it raises the question of the correlation between smart cities and low-carbon cities: is it positive, negative, or non-existent? Should the pace of smart city development be slowed to achieve sustainable urban development goals, considering the significant carbon dioxide emissions resulting from current technological choices, social habits, and policy frameworks? To address these practical issues, it is first essential to conduct an objective and accurate assessment of urban SCP and LCL. However, due to the complexity and diversity of urban carbon emissions sources, current measurement and estimation techniques fail to capture all emission types. This limitation hampers the ability to obtain comprehensive, accurate, and timely city-level carbon emission data 30 , 31 . To address this challenge, this paper decomposes smart cities and low-carbon cities into their interdependent and interactive subsystems (i.e., economic, political, cultural, social, and ecological) viewed through the lens of urban complex systems. It then develops evaluation models for both city types and conducts empirical analyses in 52 representative Chinese pilot cities. Based on these analyses, the paper elucidates the coupling coordination degree between SCP and LCL and proposes a specific pathway for their coordinated development.

This paper is therefore structured as follows: “ Literature review ” section offers an overview of the relevant literature, laying the foundation for the introduction of SCP and LCL. Subsequently, SCP and LCL are identified clearly, and measurement based on a mixed method for the coupling coordination degree is established in “ Methodology ” section, followed by a case demonstration for the introduced method in “ Results ” section and the demonstration results analysis in “ Discussions and implications ” section. Finally, “ Conclusions ” section summarizes the study’s main findings and contributions, discusses its limitations, and suggests directions for future research.

Literature review

Evaluation of smart city: contents, methods, and subjects.

The evaluation of smart cities is a central research area within the smart city development field. Developing standardized evaluation criteria serves the dual purpose of defining smart city development boundaries and scientifically measuring its effectiveness. This, in turn, facilitates the achievement of development goals centered on evaluation-driven construction, improvement, and management 32 . We conducted data collection on “smart city*” AND “evaluation”, resulting in the selection of 82 articles. This involved an extensive search of the Wos Core Collection database for articles published in the period from January 2019 to January 2024.

To facilitate a clearer understanding for readers of current research on smart city evaluation, we have categorized it by evaluation contents , evaluation methods , and evaluation subjects .

Cluster1-evaluation contents (what to evaluate), including smart city evaluation dimensions and indicators. By analyzing the article content, it’s clear that most smart city evaluation approaches align with six core dimensions: economy, quality of life, governance, people, mobility, and environment 13 , 15 . Centered around these six dimensions, international organizations (ISO, ETSI, UN, and ITU) and scholars have established various sets of smart city evaluation indicators, considering the interdependencies among urban economic, environmental, and social factors, all in alignment with the goals of sustainable urban development 32 , 33 , 34 . Notably, Sharifi 35 compiled a comprehensive list of indicators incorporating a wide range of assessment schemes. This list not only covers the scope of the evaluation indicators (project/community/city) and their data types (primary/secondary) but also considers the stages of smart city development (planning/operation) and stakeholder involvement 36 . Subsequent research predominantly utilizes the same criteria as Sharifi 35 to identify indicator sets, taking into account the specific needs of each city and defining the spatial and temporal scales of the indicator sets 37 .

Cluster 2-evaluation methods (How to evaluate) , including smart city evaluation methods and tools. Research in this field focuses on three main areas: identifying evaluation indicators for smart cities, computing composite index, and developing evaluation models 38 , 39 . Methods for indicator identification mainly include literature review, case studies, brainstorming, the Delphi method, and data-driven techniques 40 , 41 . The Analytic Hierarchy Process (AHP) is commonly used for calculating composite indices, yet it faces issues like subjective biases and data size limitations 42 . Alternative methods, such as the Analytical Network Process (ANP) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL), are used to address these drawbacks by simulating inter-indicator interactions. Additionally, techniques like Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA) are applied for indicator weighting. Finally, smart city evaluation models are constructed to aggregate various dimensions and indicators into a unified score, facilitating project comparison and ranking, and highlighting areas needing improvement 43 , 44 .

Cluster 3-evaluation subjects (Who performs the evaluation) , including smart city stakeholders and participants. Smart city evaluations involve various stakeholders and participants. These complex processes see each entity, including government agencies, international organizations, academic institutions, industry sectors, and NGOs, contributing to the smart cities’ planning, development, and management 45 , 46 . Key organizations in this realm are the International Organization for Standardization (ISO), International Telecommunication Union (ITU), United Nations Human Settlements Programme (UN-Habitat), Smart Cities Council, European Institute of Innovation and Technology (EIT Urban Mobility), and World Council on City Data (WCCD). Additionally, numerous countries have established their own smart city evaluation standards to direct and review smart city progress 11 . Notable examples are the “One New York: The Plan for a Strong and Just City” in the USA, the “BSI PAS 180” in the UK, Singapore's “Smart Nation Initiative”, and China’s “National New-type Smart City Evaluation Indicator System”.

Evaluation of low-carbon city: contents, methods, and subjects

As more countries integrate low-carbon city development into their national strategies and plans, conducting scientific evaluations of cities’ current low-carbon development levels to encourage them to adopt corresponding measures for improvement has become a key strategy in advancing cities towards a low-carbon future 47 . In the Wos Core Collection database, we conducted a search for studies spanning January 2018 to January 2023 with “low-carbon city*” AND “evaluation” as keywords, subsequently identifying 98 pertinent articles through two rounds of screening.

This section, maintaining the research framework of “ Evaluation of smart city: contents, methods, and subjects ” section ( evaluation contents, methods, and subjects ), organizes low-carbon city research to enable comparison with smart city evaluations.

Cluster 1-evaluation contents (what to evaluate), including low-carbon city evaluation systems, dimensions, and indicators. Current research focusing on low-carbon cities primarily spans six key domains: urban low-carbon scale, energy, behavior, policy, mobility, and carbon sinks. The evaluation dimensions for low-carbon cities are mainly divided into two types: single-criterion systems concentrating on specific low-carbon aspects (such as low-carbon economy, low-carbon energy, etc.), and comprehensive multi-criteria systems assessing the overall urban low-carbon development 48 , 49 . Compared to single-criterion evaluation systems, comprehensive and multi-criteria evaluation systems are increasingly gaining attention from scholars. These scholars share the view that low-carbon city construction is a diverse, dynamic, interconnected process that requires comprehensive consideration of various urban aspects, including economy, society, and environment, and involves coordinating the actions of different stakeholders to achieve sustainable urban development 50 , 51 . Additionally, international institutions and many national governments have also published low-carbon city evaluation frameworks from the perspective of comprehensive and multi-criteria evaluation systems. The most notable examples include the United Nations Commission on Sustainable Development, which set 30 indicators from four dimensions: social, environmental, economic, and institutional, to evaluate the level of urban low-carbon development. The Chinese Academy of Social Sciences proposed the “China Low Carbon City Indicator System”, covering 8 dimensions such as economy, energy, facilities, and 25 specific indicators including energy intensity, per capita carbon emissions, and forest coverage rate.

Cluster 2-evaluation methods (How to evaluate) , including low-carbon city evaluation methods and tools. Firstly, identifying evaluation indicators as the initial step in constructing a low-carbon city evaluation model, current research methods not only include traditional methods like literature review and expert interviews but also increasingly involve scholars using dynamic perspectives based on urban complex systems, applying models like DPSR (Driving forces-Pressures-State-Response), STIRPA (Stochastic Impacts by Regression on Population, Affluence, and Technology), the Environmental Kuznets Curve (EKC), and STEEP (Social, Technological, Economic, Ecological, and Political) for indicator identification 52 , 53 . Secondly, weighting evaluation indicators, an essential part of model construction, typically involves methods like subjective weighting (expert scoring, Delphi method, AHP) 54 , objective weighting (PCA, Entropy weight method, variance analysis), and combined weighting (DEA) 55 . Each method has its characteristics and suitable scenarios and should be selected according to specific circumstances. Additionally, quantitative assessment of regional carbon emissions using methods like carbon footprint analysis, baseline emission comparison, and Life Cycle Assessment (LCA) is also becoming a research focus 56 .

Cluster 3-evaluation subjects (Who performs the evaluation) , including low-carbon city stakeholders and participants. The evaluation of low-carbon cities also involves multiple stakeholders (government, enterprises, residents, etc.) 57 . Among them, international organizations like the International Organization for Standardization (ISO), the International Energy Agency (IEA), and the World Meteorological Organization (WMO) have played significant roles in establishing low-carbon city evaluation standards and promoting global low-carbon city development. Additionally, due to economic, policy, and perception factors, current low-carbon city construction relies primarily on government financial input, with social capital and public participation in low-carbon city construction noticeably lacking 58 . Therefore, how to enhance the awareness of enterprises and residents as main actors in low-carbon city construction has become a current research focus.

Coupling coordination analysis between SCP and LCL

Smart cities and low-carbon cities, as important urban development models for the future, have seen an increasing focus on their interrelation by scholars in recent years, becoming an emerging research hotspot in the field. In the Wos Core Collection database, we searched for studies from January 2018 to January 2024 using the keywords “smart city*” “low-carbon city*” “correlation analysis” “coupling coordination analysis” and “urban sustainability”. After two rounds of screening, 24 related studies were selected for analysis.

From the perspective of research results, the current research conclusions about the correlation between low-carbon cities and smart cities primarily include two main points: (i) SCP and LCL cannot achieve coupling coordination development. Some scholars argue that SCP and LCL differ in their focus: SCP emphasizes urban technological and economic development, while LCL focuses more on urban ecological construction 17 . Particularly, De Jong identified 12 urban development concepts, including smart city, low-carbon city, eco-city, and green city. He believes that a clear distinction must be made in the conceptual definition of these types of cities to more accurately guide future urban planning 59 . Furthermore, some scholars argue that the relationship between SMC and LCC is negatively correlated. Deakin believes that the direct environmental benefits of IoT technology are insufficient to achieve urban sustainability goals 60 . Barr et al. argue that the logic of smart cities often leads city administrations to prioritize superficial changes and promote individual behavioral shifts, detracting from the crucial task of reconfiguring urban infrastructure for low-carbon lifestyles 61 , 62 . (ii) SCP and LCL can achieve coupling coordination development. Some scholars believe there is a positive correlation between SCP and LCL, with SCP potentially promoting the development of LCL. Specifically, the intelligent systems built by SCP can effectively match urban energy supply and demand, reducing urban carbon emissions, such as through smart grids and intelligent transportation networks 18 . It is worth noting that most of the studies on the coupling coordination relationship between urban SCP and LCL are based on perspectives of individual urban subsystems such as technology, economy, management, industrial structure, and society. They lack a comprehensive consideration of the city as a complex system 59 , 61 , 63 .

From the perspective of research methodologies, coupling coordination analysis is a fundamental statistical approach for examining relationships between two or more variables. This analysis typically employs techniques such as Pearson’s correlation coefficient, Spearman’s rank correlation coefficient, Kendall’s tau, partial correlation, point-biserial correlation, and multiple correlations. Each technique offers unique insights into the nature and strength of the interdependencies among variables 61 . The selection of an appropriate method depends on the data type (continuous, ordinal, or categorical), its distribution (e.g., normal distribution), and the specific objectives of the research.

In summary, although existing research has made significant contributions to the independent evaluation and advancement of smart cities and low-carbon cities, including their relevant construction content, main actors, as well as some specific measures such as empowering cities with data intelligence for low-carbon economic development and transitioning industrial structure to low-carbon, there are still some important knowledge gaps. On the one hand, current research primarily analyzes the coupling coordination relationship between urban SCP and LCL from the micro-perspective of individual urban subsystems such as economic and energy systems. This approach lacks a macroscopic perspective from the complex urban system, which is detrimental to the comprehensive development of cities 60 , 64 , 65 . On the other hand, current studies often only conduct basic qualitative comparisons of the relationship between the development levels of urban SCP and LCL from a quantitative or qualitative perspective. They lack a comprehensive analytical approach that integrates both qualitative and quantitative analyses for further exploration of the coupling coordination relationship between urban SCP and LCL. This shortfall hinders the sustainable development of cities.

To fill these knowledge gaps, this study employs a mixed-methods approach, combining qualitative and quantitative analyses, to examine the model of coupling coordination between urban SCP and LCL. It also develops recommendations to enhance this coupling coordination, aiming to support sustainable development goals. Furthermore, this research selects 52 typical low-carbon and smart pilot cities in China as case studies, ensuring both scientific validity and practical applicability of the findings. Additionally, to enhance the logical coherence and readability of this study, we posit that a coupling coordination relationship exists between urban SCP and LCL and thus propose Hypothesis 1 .

Hypothesis 1

There is a substantial degree of coupling coordination between the overall urban system’s SCP and LCL, yet there are disparities in this coordination degree among the subsystems of economy, society, politics, culture, and ecology.

Methodology

Research framework.

The construction of low-carbon and smart cities, as key pathways to urban sustainability, necessitates examining their interplay and fostering their collaborative development for achieving sustainability goals 66 . This research employs a sequential framework, including Conceptual, Data, Analysis, and Decision-making Layers, to methodically explore the coupling coordination relationship between SCP and LCL, with the framework illustrated in Fig. 1 .

Research framework.

Firstly , in the Conceptual Layer, this study aligns with the United Nations’ objectives for sustainable cities, encompassing economic growth, social equity, better life conditions, and improved urban environments. Integrating these with China’s “Five-Sphere Integrated Plan (economy, politics, culture, society, and ecological environment construction)” for urban development, the research dissects the components of smart city systems (such as information infrastructure, information security, public welfare services) and low-carbon city systems (including low-carbon construction, transportation, and industry), with the aim to collect indicators. Secondly , in the Data Layer, this research develops smart city and low-carbon city evaluation systems, grounded in national standards and official statistics, to qualitatively examine the correlation between SCP and LCL from a macro perspective. Thirdly, in the Analysis Layer, this study selects 52 cities, both smart and low-carbon pilot cities in China, as samples for quantitative analysis. The process involves standardizing indicators, scoring and ranking the cities based on their smart performance and low-carbon levels, followed by employing Pearson’s correlation coefficient and coupling coordination degree model to scientifically analyze the correlation between SCP and LCL. Finally, in the Decision-making Layer, the study examines the coupling coordination relationship between urban smart performance, the overall low-carbon level, and the low-carbon level across five dimensions, which is key for us to test Hypothesis 1 . It also formulates development paths for the coupling coordination of smart and low-carbon cities.

SCP index system construction

Since the concept of smart cities was introduced in 2008, many national governments have established smart city evaluation standards. Due to varying national conditions, SCP evaluation indicators differ across countries. As the sample cities in this study are Chinese smart pilot cities, the selection of SCP evaluation indicators primarily references relevant Chinese national standards. As a global pioneer in smart city development, China released the “Evaluation indicators for new-type smart cities (GB/T 33356-2016)” in 2016 and revised it in 2022. This national standard, with its evaluative indicators, clearly defines the key construction content and development direction of new smart cities, aiming to specifically enhance the effectiveness and level of smart city construction, gaining significant recognition within the industry.

This study, grounded in the concept of a city’s “Five-in-One” sustainable development, is guided by three principles of “Inclusive well-being & Ecological harmony”, “Digital space & Physical space”, and “New IT technologies & Comprehensive services”. It also adheres to the “people-oriented concept” and adopts an “urban complex dynamic perspective” in the process of smart city construction. Additionally, it follows the principle of “similar attributes of evaluation objects”. Based on these foundations, the study establishes three criteria for selecting evaluation indicators, including scientific, coordination, and representation. Drawing on the Chinese government’s smart city evaluation standards and utilizing a literature review methodology, this research constructs an SCP evaluation indicator system for cities, as detailed in Supplementary Appendix Table A1 . The SCP index system includes six primary indicators, including smart public service (SPE), precise governance (PG), information infrastructure (II), digital economy (DE), innovative development environment (IDE), and citizen satisfaction (SCS). It also features 24 secondary indicators, such as traffic information services, grassroots smart governance, and spatio-temporal information platforms. Importantly, to explore the correlation between smart cities and low-carbon cities more effectively, the study deliberately omits “Internet + Green Ecology” related indicators from the smart city evaluation system. To ensure the accuracy and representativeness of these indicators, they were validated through expert consultation, public participation, and comprehensive statistical methods.

LCL index system construction

Current international organizations and academic perspectives on low-carbon city evaluation systems are predominantly based on the urban complex systems approach, considering the interplay and interaction of aspects such as low-carbon society, economy, and technology. Consistent with the principles for selecting SCP evaluation indicators, the choice of LCL evaluation indicators in this study primarily adheres to relevant Chinese national standards and related literature.

As a proactive practitioner in global low-carbon city development, in 2021, the Chinese government released the “Sustainable Cities and Communities—Guides for low-carbon development evaluation (GB/T 41152-2021)”. This national standard evaluates the level of urban low-carbon development, clarifying the key directions for such development, and serves as a current guide for low-carbon city construction in China. Thus, this study, grounded in the “Five-in-One” sustainable urban development framework and guided by the principles of “carbon reduction & pollution reduction”, “green economic growth”, and “enhanced carbon sequestration capacity”, combines the previously established principles of scientific, coordination, and representative for selecting evaluation indicators. It establishes an LCL index system based on the Chinese government’s evaluation standards and relevant literature. Specifically, the LCL evaluation index system constructed in this study includes five primary indicators, including low-carbon economic (LCE), low-carbon society (LCS), low-carbon environmental quality (LCEQ), low-carbon management (LCM), and low-carbon culture (LCC), as well as 22 secondary indicators such as energy consumption per unit of GDP and carbon emission intensity, as shown in Supplementary Appendix Table A2 . Similarly, to ensure the accuracy and representativeness of the indicators, the specific indicators were validated through expert consultation, public participation, and comprehensive statistical methods.

Analysis model construction

In this study, an Entropy-TOPSIS-Pearson correlation-Coupling coordination degree (ETPC) analysis model is constructed to quantitatively analyze the coupling coordination relationship between Urban SCP and LCL. The entropy method is first applied for objective weighting of evaluation indices, ensuring data objectivity and reducing subjective bias, thus enhancing the model’s accuracy and fairness. Next, the TOPSIS method is used to rank sample cities based on their smart performance and low-carbon levels, providing a straightforward and intuitive ranking mechanism. The Pearson correlation method then examines the correlation between SCP and LCL, offering data-driven insights into the dynamic relationships between these variables. Finally, the coupling coordination model calculates the degree of coordination between SCP and LCL, providing a theoretical basis for subsequent enhancement pathways and policy recommendations. The ETPC model constructed in this study has several advantages and complementarities, allowing for a comprehensive analysis and evaluation of the research question from various perspectives. Additionally, the ETPC model can be broadly applied to other multidimensional evaluation and decision analysis issues, such as the coupling coordination between various public health interventions and community health levels, and the comprehensive effects of different economic policies on regional economic development and environmental impact. Specific analysis steps are outlined as follows.

Step 1: Conduct the data normalization process.

where x ij and y ij represent respectively the original and standardized value for the indicator j in referring to the sample case i ( i = 1,2,3,…, m; j = 1,2,3,…, n ), max (x j ) and min (x j ) denote respectively the largest and smallest value among all m samples for the indicator j , P ij represents the value proportion of indicator j in the sample case i to the summation value of the indicator from all cases.

Step 2: Calculate the weight and measure the comprehensive level based on entropy method.

The entropy weight method, an objective approach deriving weights from sample characteristics, mitigates expert bias, enhancing the objectivity and credibility of indicator weighting 67 . This study employs this method, determining weights through the calculation of each indicator’s information entropy, and measure the comprehensive level of the subsystem.

where m is the total number of sample cases, ${e}_{j}$ demonstrates the entropy value of the j indicator and ${\omega }_{j}$ denotes the weight of indicator j , and V represent the comprehensive level.

Step 3: Conduct a ranking of evaluation objects based on TOPSIS method.

A key limitation of the entropy method is its tendency to neglect the significance of indicators. The TOPSIS method, addressing this issue, is an ideal-solution-based ranking technique that aids in multi-objective decision-making among finite options 68 . In this approach, the study first determines positive and negative ideal solutions, measures each objective’s distance to these ideals, and subsequently ranks the subjects by the proximity of each objective to the ideal solution.

where ${ V}^{+}$ and ${V}^{-}$ respectively represent the best ideal solution and the worst ideal solution, ${D}_{i}^{+}$ and ${D}_{i}^{-}$ represent the distances from the objective to the positive and negative ideal solutions, respectively. ${C}_{i}$ indicates the closeness of the evaluation objective to the optimal solution, with ${C}_{i}\in \left[\text{0,1}\right]$ . A larger ${C}_{i}$ value suggests stronger smart and low-carbon development capabilities of the sample city.

Step 4: Analyze the correlation based on Pearson correlation method.

The Pearson correlation method is commonly used to measure the correlation coefficient between two continuous random variables, thereby assessing the degree of correlation between them 69 . In this study, based on the results from Steps 1–3, two sets of data are obtained representing the smart development level and low-carbon development level of sample cities, $A:\left\{{A}_{1},{A}_{2},\dots ,{A}_{n}\right\}$ and $B:\left\{{B}_{1},{B}_{2},\dots ,{B}_{n}\right\}$ . The overall means and covariance of both data sets are calculated, resulting in the Pearson correlation coefficient between the two variables.

where ${A}_{i}$ and ${B}_{i}$ respectively represent the SCP and LCL of sample cities. $E\left(A\right)$ and $E\left(B\right)$ are the overall means of the two data sets, ${\sigma }_{A}\text{ and }{\sigma }_{B}$ are their respective standard deviations, $cov(A,B)$ is the covariance, and ${\rho }_{AB}$ is the Pearson correlation coefficient. When the correlation coefficient approaches 0, the relationship weakens, as it nears − 1 or + 1, the correlation strengthens.

Step 5: Analyze the coupling coordination degree based on the coupling coordination model.

The coupling coordination degree characterizes the level of interaction between different systems and serves as a scientific model for measuring the coordinated development level of multiple subsystems or elements 70 . This study has developed a model to measure the coupling coordination degree between two systems.

where C defines the coupling degree, ${f}_{1}$ and ${f}_{2}$ are the evaluation values of SCP and LCL respectively. CPD represents the coupling coordination degree. $\alpha$ , $\beta$ are the coefficient to be determined, indicating the importance of the systems. This study assumes that each system is equally important. Thus $\alpha =\beta =1/2.$

In this study, building upon the framework established by a preceding study, a classification system for the coupling coordination degree was developed. This system delineates the various types of coupling-coordinated development among SCP, LCL, LCS, LCM, LCEQ, and LCC. Current research on the division of coupling coordination degree intervals often uses an average distribution within the [0, 1] range 70 . However, due to the large sample size and the wide distribution range of coupling coordination degrees in this study, we have categorized these types into ten distinct levels based on their rank, as detailed in Table 1 .

Selection of sample cities and data collection

The Chinese government has prioritized the development of smart and low-carbon cities. Since 2010, it has launched 290 smart city pilots and 81 low-carbon city pilots across various regions, reflecting different levels of development, resource allocations, and operational foundations. To maintain the scientific integrity of our study, we established stringent criteria for selecting sample cities: (i) each city must be concurrently identified as both a smart and a low-carbon city pilot, and (ii) their government agencies must have issued data on key performance indicators for these initiatives. Following these criteria, our research has ultimately selected 52 cities as samples, as detailed in Fig. 2 . It is noteworthy that these 52 typical case cities are almost all provincial capitals in China, mostly located within the Yangtze River Delta, Pearl River Delta, Jingjinji (Beijing–Tianjin–Hebei), and Western Triangle economic regions. Additionally, according to the “Globalization and World Cities Research Network (GaWC) World Cities Roster 2022 (GaWC2022)”, these cities are ranked within the top 200 globally. Therefore, given the scope of this research, these case cities offer significant representativeness and can serve as valuable models for promoting development in other urban areas. The data for this paper were sourced from the “China Low-Carbon Yearbook (2010–2023)”, the “China Environmental Statistics Yearbook (2010–2023)”, and low-carbon city data published by the governments of the sample cities. Additionally, this study addressed any missing data by averaging the data from adjacent years and applying exponential smoothing.

52 sample cities and their geographic locations.

Weighting values between evaluation indicators

The entropy weighting values between the 20 indicators of SCP and the 19 indicators of LCL are calculated by applying the data described in “ Weighting values between evaluation indicators ” section to formula ( 1 )–( 5 ), and the results are shown in Supplementary Appendix Tables A3 and A4 . Specifically, within the SCP evaluation framework, SPE and II are assigned the highest weights, while LCS and LCM are allocated the highest weights within the LCL evaluation framework. Conversely, SCS and LCC have attributed the lowest weights in their respective contexts.

Evaluation of SCP and LCL in sample cities

Utilizing the data from “ Selection of sample cities and data collection ” section and the weighting values derived in “ Weighting values between evaluation indicators ” section, we can determine the SCP and LCL of sample cities using the TOPSIS method, as outlined in formulas ( 6 )–( 9 ). The results are illustrated in Supplementary Appendix Table A5 and Fig. 3 . In this study, the value of the closeness coefficient (C i ) is used to indicate the relative closeness of a particular sample city to the negative ideal point 71 . The negative ideal point represents the worst solution of the ideal, where the individual attribute values reach their worst in each alternative. Therefore, a larger value of closeness indicates better smart city performance or a lower carbon level of a sample city 72 . C LCL and C SCP respectively represent the low-carbon level closeness coefficient and the smart city performance closeness coefficient. In referring to Supplementary Appendix Table A5 , the best three cities of SCP are Shenzhen, Shanghai, and Hangzhou, whilst the worst three cities are Yan’an, Jincheng, and Xining. Furthermore, Chengdu, Qingdao, and Beijing are the best there low-carbon level performers. Whilst Jincheng, Urumqi, and Huhehaote are the three worst.

TOPSIS-based analysis of SCP with LCL in 52 sample cities.

In referencing Fig. 3 , this study considers SCP data of sample cities as the control variable and ranks them in ascending order based on TOPSIS results. We then examine changes in LCL data to ascertain the correlation between these variables, yielding two key research conclusions: on one hand, analysis of 52 sample cities demonstrates a general ascending trend in both SCP and LCL data curves. This trend suggests a positive correlation between these two parameters. On the other hand, the LCL data, in contrast to the consistent rise in SCP, exhibits notable fluctuations and wider dispersion. This indicates that the positive correlation between SCP and LCL, while present, is not markedly robust.

Correlation results of SCP and LCL in sample cities

Correlation analysis of urban SCP and overall-LCL. This analysis employs the closeness coefficient (C i ) to assess SCP and overall-LCL in sample cities for Hypothesis 1 in Eqs. ( 10 ) and ( 11 ). The results are presented in Table 2 . Additionally, a linear regression analysis is conducted to determine the presence and magnitude of the relationship between SCP and LCL in these cities, as shown in Fig. 4 .

The scatter and regression of SCP and LCL: ( A ) SCP & Overall-LCL; ( B ) SCP & LCM; ( C ) SCP & LCS; ( D ) SCP & LCE; ( E ) SCP & LCQE; ( F ) SCP & LCC.

Considering the closeness coefficient range, correlation is categorized into five levels: very weak ( $\left|{\rho }_{AB}\right|<0$ .1), weak ( $0.1\le \left|{\rho }_{AB}\right|<0$ .3), moderate ( $0.3\le \left|{\rho }_{AB}\right|<0$ .5), strong ( $0.5\le \left|{\rho }_{AB}\right|<0$ .7), and very strong ( $0.7\le \left|{\rho }_{AB}\right|<1.0$ ) 73 . Table 1 indicates a strong positive correlation between SCP and overall LCL. Linear regression analysis in Fig. 4 A demonstrates a significant correlation between SCP and urban LCL ( R 2 = 0.42, p < 0.001), with notable differences exist among cities, consistent with Hypothesis 1 .

Correlation analysis of SCP and each low-carbon dimension. Pearson correlation analysis effectively measures the strength of linear relationships between two variables, but it does not identify causal relationships between them. To address this limitation and explore the interaction between the two variables, this study sets and solves the closeness coefficient for each low-carbon dimension, which are low-carbon economy (C LCE ), low-carbon society (C LCS ), low-carbon environmental quality (C LCEQ ), low-carbon management (C LCM ), and low-carbon culture (C LCC ). It then calculates the correlation analysis results for SCP and each low-carbon dimension for Hypothesis 1 , as shown in Table 1 . Furthermore, the results of the linear regression analysis are presented in Fig. 4 .

In detail, strong correlations exist between SCP and LCM, LCS, and LCEQ. The correlation is moderate with LCE and weak with LCC. Furthermore, linear regression analysis shows that the links between SCP and low-carbon levels across five dimensions are significant with minimal variance. Cities with higher SCP typically show higher values in LCM ( R 2 = 0.38, p = 0.000), LCS ( R 2 = 0.35, p = 0.000), and LCE ( R 2 = 0.32, p = 0.000) as depicted in Fig. 4 B–D. However, this trend is less pronounced in LCEQ ( R 2 = 0.17, p = 0.000) and LCC ( R 2 = 0.06, p = 0.001), which exhibit greater dispersion as shown in Fig. 4 E,F. The lower R 2 values for LCEQ and LCC compared to other dimensions suggest a greater influence of factors not included in the model. Furthermore, to ensure the credibility and reliability of the research findings, this study conducted a sensitivity analysis by identifying and removing outliers from the sample dataset using the Z-score method, in addition to the previously mentioned Pearson correlation analysis. The Pearson correlation coefficient for the original dataset of city SCP and LCL is 0.65, with a significant P-value. After removing the outliers, the Pearson correlation coefficient is 0.61, and the P-value remained significant. Therefore, the correlation between city SCP and LCL proposed in Research Hypothesis 1 is robust.

Coupling coordination degree of SCP and LCL in sample cities

The degree of coupling coordination comprehensively considers multiple aspects of urban complex systems, including economic, social, and environmental dimensions. By systematically evaluating the coordinated development level of urban SCP and LCL, this approach enables the analysis of the coupling and coordination relationships between SCP and LCL, as well as among various subsystems such as LCM, LCS, LCE, LCEQ, and LCC. This reveals the dynamic interactions and causality between SCP and LCL within urban complex systems. The coupling coordination degrees of SCP and LCL, along with their subsystems, in 52 typical smart and low-carbon pilot cities in China, are illustrated in Fig. 5 .

Coupled coordination degree of SCP and LCL, LCS, LCEQ, LCE, LCM, LCC.

Characteristics of objective changes in the coupled coordination degree between SCP and LCL. Based on the coupling coordination model and Eqs. ( 12 ) to ( 14 ), the coupling coordination degree of the urban complex system in SCP and LCL regions is calculated for Hypothesis 1 , as illustrated in Fig. 5 .

From the holistic perspective of urban complex systems, as the level of urban SCP continuously improves, the coupling coordination degree between SCP and LCL among 52 pilot cities in China shows an upward trend. This indicates that as the functional indices of urban SCP and LCL both strengthen, their interaction and coordination also enhance. Among these, Jincheng has the lowest coupled coordination degree at 0.5201, while Beijing boasts the highest at 0.8622. Within the 52 pilot cities, 5.78% exhibit a barely coupling coordination level, 51.93% display a primary coupling coordination level, 25% achieve an intermediate coupling coordination level, and 17.31% reach a good coupling coordination level. Moreover, the average coupling coordination degree of the 52 pilot cities is 0.598, suggesting that the SCP and LCL of the pilot cities can achieve coupled coordinated development.

Characteristics of objective changes in the coupled coordination degree among SCP, LCM, LCS, LCE, LCEQ, and LCC for Hypothesis 1 are illustrated in Fig. 5 .

From the perspective of urban subsystems, the coupling coordination degrees of LCS & SCP, LCE & SCP, and LCM & SCP all exhibit characteristics of steady fluctuations with an upward trend, while the coupling coordination degree of LCC & SCP shows greater volatility in its upward trend. The coupling coordination degree of LCEQ & SCP demonstrates a trend of initially rising and then declining. Furthermore, the average values of the coupling coordination degrees for LCS & SCP, LCE & SCP, LCM & SCP, LCEQ & SCP, and LCC & SCP are 0.478, 0.761, 0.779, 0.710, and 0.485, respectively. Among these, the pilot cities’ subsystems of LCE, LCM, and LCEQ with SCP exhibit an intermediate level of coupling coordination, while the coupling coordination degrees of LCS and LCC with SCP are on the verge of a dysfunctional recession. This indicates that the causal relationships between urban SCP and the subsystems of urban LCM, LCS, LCE, LCEQ, and LCC vary. Overall, Hypothesis 1 holds true both from the perspective of the city's overall system and from the perspective of its various subsystems.

Discussions and implications

Relationship between scp and lcl of different cities.

Considering the evaluation results of the urban SCP and LCL, four grades of the overall points can be classified, namely, excellent (0.7–1.0), average (0.5–0.7), below average (0.4–0.5), and poor (0–0.4). Subsequently, the sample cities in Supplementary Appendix Table A5 were classified based on these gradations. In the sample, cities with excellent SCP constitute 9.62%, about double the proportion with excellent LCL. Cities with average SCP account for 48.08%, whereas those at average LCL represent only 26.92%. Notably, cities with poor LCL comprise 26.92%, nearly triple the rate of those with poor SCP. The findings suggest that China’s SCP currently outperforms its low-carbon city initiatives, largely attributable to the rapid advancement of the Internet and Information and Communication Technology (ICT) in recent years. What’s more, Fig. 4 illustrates that urban SCP significantly positively influences the urban LCL, though substantial variations exist among different cities. The relevant types can be summarized into the following four categories.

Quadrant I-high SCP and high LCL, including only six cities (Shenzhen, Shanghai, Beijing, Ningbo, Xiamen, and Qingdao). These cities are not only among China’s earliest smart city pilots but also recent focus areas for the government’s “Carbon Peak Pioneer Cities” initiative. By actively exploring innovative models, systems, and technologies for smart and low-carbon co-development, these cities provide valuable practical experiences for others. For instance, Shenzhen has developed a multi-level, multi-component greenhouse gas monitoring network and technology system for “carbon flux, carbon concentration, carbon emissions”, while Ningbo has constructed a “smart zero-carbon” comprehensive demonstration port area.

Quadrant II-poor SCP and poor LCL, numerous cities in Fig. 4 A, such as Jincheng, Lhasa, and Urumqi, exhibit poor SCP and LCL. Despite China having the most smart and low-carbon city pilots globally, its development level in these areas still lags significantly behind typical developed countries. While China’s infrastructure like networking and computing power has reached a certain scale, issues persist with insufficient integration and intensity in infrastructure construction and operation, as well as problems with aging infrastructure and low levels of intelligence. Furthermore, although China’s low-carbon pilot cities have made positive progress in promoting low-carbon development, most still have incomplete carbon emission statistical systems and inadequate operational mechanisms, leading to generally poor overall low-carbon development levels.

Quadrant III-high LCL but poor SCP, such as Kunming, Xining, and Guiyang. These cities possess resources conducive to low-carbon development, such as Kunming and Guiyang with their rich forest carbon sinks, and Xining with abundant clean energy sources like solar and wind power. However, they are mostly situated in China’s central and southwest areas with underdeveloped physical and economic conditions. Leveraging their abundant low-carbon resources, and utilizing big data and IoT technology, achieving sustainable green economic growth through carbon credits and trading markets, as well as green finance, represents a significant future development direction for these cities.

Quadrant IV-high SCP but poor LCL, including Suzhou, and Jinhua Zhongshan, decoupling economic development from carbon emissions presents a significant development challenge for these cities. Specifically, for Suzhou, one of the world’s largest industrial cities, the main challenge is achieving decarburization in the energy sector and transitioning high-emission manufacturing industries to low-carbon alternatives.

What’s more, as illustrated in Fig. 5 , the degree of interaction between SCP and LCL across the 52 pilot cities in China positively impacts the balanced and comprehensive performance of these cities. This, in turn, fosters the coordinated development of urban systems as a whole. Moreover, the continual increase in the coupled coordination degree between SCP and LCL with the enhancement of SCP in pilot cities indicates that smart city construction contributes to urban low-carbon development. Future urban development in China should fully leverage the industrial upgrading effect, carbon sequestration effect, and energy utilization effect of smart city construction. However, the increasing slope of the SCP & LCL coupled coordination degree curve in Fig. 5 suggests significant regional differences in the level of SCP & LCL coupled coordination development across Chinese cities. Smart city construction has a more pronounced decarburization effect in central and western cities, southern cities, non-environmentally focused cities, and resource-based cities, with cities in the northwest showing notably poorer levels of SCP & LCL coupled coordination development. This serves as a warning for future urban development in China.

Relationships between SCP and LCL in each urban subsystem

The relationship between urban SCP and LCL across five dimensions is illustrated in Fig. 4 B–F. There is a strong positive correlation between SCP and LCM, LCS, and LCE, while a moderate correlation is observed with LCEQ, and a weak correlation with LCC. Furthermore, the degree of coupling coordination between SCP and subsystems such as LCS, LCEQ, LCE, LCM, and LCC is examined in Fig. 5 . The results of the coupling coordination vividly illustrate the synergistic interactions and developmental harmony between urban SCP and various systems.

Among these, the coupling coordination degree curve fluctuation between SCP & LCM is stable, situated at an intermediate coupling coordination level, indicating the dominant role of the Chinese government in the construction of smart cities and low-carbon cities, as well as the effectiveness of policy implementation. However, this also suggests that in promoting urban smart and low-carbon construction, China faces the risk of adopting “one-size-fits-all” mandatory policies, neglecting to advance construction in phases with emphasis, tailored to the city's resource endowment and economic development status. The coupling coordination degree curve changes between SCP&LCE and SCP&LCL show the highest degree of fit, indicating that low-carbon economic development brought about by digital empowerment and upgrading of the urban industrial structure is a key driving factor for promoting the coupled coordination development of urban smart and low-carbon initiatives. Transforming traditional industrial structures and pursuing low-carbon upgrades of the economic structure present challenges for urban development in China today. The coupled coordination degree of SCP & LCS is on the verge of a dysfunctional recession, highlighting the imbalance in the development between China's SCP and LCS, especially in terms of new infrastructure construction, such as smart transportation and logistics facilities, smart energy systems, smart environmental resources facilities, etc. The current construction of new infrastructure in China is far from meeting the living needs of the broad masses of people.

It is noteworthy that with the continuous improvement of the SCP in sample cities, the coupling performance degree between SCP and LCEQ exhibits two phases: an initial stage of synergistic enhancement followed by a stage of diminished synergy. In the early phase of synergistic development, the SCP and LCEQ systems of cities, driven by shared goals of sustainable urban development, strategy adjustments, resource sharing, and technological progress, facilitated effective collaboration and integration between systems. However, upon reaching a certain stage, intensified resource competition, declining management efficiency, and environmental changes led to internal system fatigue, resulting in weakened synergy. This indicates that once the technological effects generated by smart city construction reach a certain level, it becomes crucial to enhance the city's capacity for autonomous innovation. Addressing the bottleneck issues of core technologies and transforming the development mode of smart low-carbon technology from “imitative innovation” represent significant breakthroughs for further promoting the coupled coordination of SCP and LCEQ in China’s future.

Moreover, as the SCP of sample cities continuously improves, the coupled coordination degree between SCP and LCC shows two phases: initial stable fluctuations and subsequent rapid growth. The turning point in the curve change occurs at a coupled coordination degree of 0.6, denoted as the primary coupling coordination point. Among these, the low-carbon awareness rate of urban residents, as a key indicator of LCC, shows that the majority of urban residents in China are still in the cognitive awakening stage regarding low-carbon consciousness. At this stage, residents begin to recognize the severity of climate change and environmental degradation, along with the importance of smart low-carbon lifestyles in mitigating these issues. The government continuously promotes this awareness through media reports, educational activities, official propaganda, and community initiatives. As residents gain a deeper understanding of the issues, their attitudes shift from initial indifference or skepticism to a stronger identification with and support for the values and concepts of smart low-carbon living. This shift encourages residents to experiment with new smart low-carbon lifestyles, gradually finding suitable smart low-carbon behavioral patterns that become habitual. Ultimately, when smart low-carbon lifestyles are fully internalized as part of residents’ values, they not only practice smart low-carbon living at the individual level but also actively participate in promoting society’s smart low-carbon construction. Therefore, this study posits that the emergence of the coupled coordination degree turning point between SCP and LCC is not only a process of individual behavioral change but also a reflection of social and cultural transformation. This process is time-consuming and influenced by multiple factors, including policy guidance, economic incentives, educational dissemination, and the social atmosphere.

Implications for promoting coupling coordination development between urban SCP and LCL

Low-carbon and smartness are vital features of modern, sustainable urban development and key supports for it. This study posits that urban low-carbon and smart development should not be disjointed but rather synergistic and complementary. To better achieve sustainable urban development goals, a model should be constructed with “low-carbon” as the cornerstone of sustainable development and “smartness” as the technological assurance for low-carbon growth. Specifically, this study proposes the “urban smart low-carbon co-development model”, which entails a deep integration of intelligent technologies such as the Internet of Things (IoT) and big data with urban construction, governance services, and economic development. This model leverages digitalization to facilitate decarburization, thereby achieving urban sustainable development goals such as energy-efficient and green urbanization, ecological and livable environments, and streamlined governance services.

Furthermore, to better coordinate smart development with low-carbon city construction, enhance low-carbon city building through digitalization, and explore exemplary practices and models of smart low-carbon city construction, this study finds it necessary to establish an evaluation system for smart and low-carbon urban co-development. Therefore, based on the aforementioned urban SCP and LCL evaluation indicator system, this study initially conducted a literature review of past research, selecting 5 primary indicators and 20 secondary indicators from 48 articles to evaluate the degree of coupling coordination development between urban SCP and LCL. Subsequently, the Delphi method was employed to finalize the list of evaluation indicators, with 10 experts from various regions and diverse backgrounds in China refining the list and determining the weights of each indicator, as shown in Supplementary Appendix Table A6 . The final Smart Low-Carbon City Coupling Coordination Development Evaluation Indicator System, as presented in Table 3 , comprises 5 primary indicators and 18 secondary indicators. This evaluation system aims to emphasize the utilization of next-generation information technologies such as 5G, artificial intelligence, cloud computing, and blockchain to expand urban green ecological spaces, strengthen ecological environment governance, and enhance the level of intelligent urban governance, meeting the development needs of smart low-carbon cities.

The policy implications from the analysis results suggest that actions should be taken by government departments in China to reduce the uneven performance between urban SCP and LCL across various cities. These actions include, for example: Firstly, guiding the innovative development of urban SCP and LCL through policies, such as enhancing government digital services and administrative platforms, continuously promoting the development of emerging industries and the upgrading of traditional industries, and actively promoting green energy technologies. Secondly, categorizing and advancing the coordinated development of smart and low-carbon cities—comprehensive development should be pursued simultaneously in large cities in eastern and central China, while in smaller cities in western China, priorities should include enhancing urban innovation capabilities and improving infrastructure to lay a solid foundation for the coupled coordination of urban SCP and LCL. Thirdly, constructing a multi-stakeholder governance system to maximize the leading role of the government, the main role of enterprises, and the active participation of residents. By fostering a positive social atmosphere and cultural attributes, this will enhance the sense of participation and achievement among different social groups, creating a sustainable development model for urban SCP and LCL coordination. Lastly, emphasizing the development of SCP and LCL coordination in county-level cities is crucial. While large Chinese cities have already begun to form a pattern of coordinated SCP and LCL development, county-level cities, though with weaker infrastructures, possess tremendous potential. Focusing on low-carbon production, circulation, and consumption, and strengthening smart and low-carbon constructions in county-level cities will be a vital task for future urban development in China.

Conclusions

The global urbanization process brings opportunities for economic growth and social development, but also presents a series of challenges, such as environmental pressures and resource constraints 3 . The evaluation of urban SCP and LCL creates a link between the policy-making in urban resources environment management and the objectives of sustainable development goals (SDGs 11.4, 11.6, and 11.b) at the city level 74 . Currently, there is no unified consensus on the coupling coordination development between urban SCP and LCL. This study proposes a method combining qualitative and quantitative analysis from the perspective of urban complex systems to analyze the coupling coordination relationship between SCP and LCL. This new method clearly interprets a strong positive correlation between urban smart performance and the overall low-carbon level. Specifically, there are strong correlations between SMC and LCM, LCS, and LCE, with a moderate correlation to LCQE and a weak correlation with LCC. Several innovative insights for this method are highlighted: (i) sustainable development based on SCP and LCL assessment; (ii) emphasizing the “people-centric” concept in urban development; (iii) analyzing from the perspective of urban complex systems.

This study selected 52 typical smart and low-carbon pilot cities in China as sample cities to analyze the coupled coordination relationship between urban SCP and LCL. And the main findings from this analysis can be summarized as follows: (i) smart city initiatives outperform low-carbon city development, with notable differences in SCP and LCL effectiveness across eastern, central, and non-resource-based cities versus western, peripheral, and resource-dependent ones in China. (ii) A strong positive link between urban SCP and low-carbon levels, especially between SCP and LCM, LCS, and LCE, with moderate and weak correlations to LCEQ and LCC, respectively. (iii) An increasing urban SCP levels enhance the coupling coordination within the urban SCP and LCL system. SCP & LCE, SCP & LCM, and SCP & LCS subsystems align well with the overall system, driving the coupled coordination of urban SCP and LCL. In contrast, SCP & LCC and SCP & LCEQ have lesser alignment, affected by factors like technology, policy, economic incentives, education, and societal attitudes. Based on the evaluation results, this study posits that the development of urban low-carbon and smart initiatives should not be disjointed but rather synergistic and complementary. This study constructs an evaluation indicator system for the co-development of smart low-carbon cities aimed at better guiding the future coupling coordination development of smart and low-carbon cities.

The novelty of this study not only addresses the practical dilemma of obtaining comprehensive, accurate, and timely urban-level carbon emission data, a challenge due to existing measurement and estimation technologies being unable to capture all types of carbon emissions, but also assesses the urban SCP and LCL. Simultaneously, by combining qualitative and quantitative analysis methods, it fills the research gap on the nature of the coupled coordination relationship between urban SCP and LCL. Moreover, from the perspective of urban complex systems, this study dissects the urban low-carbon level into LCC, LC, LCE, LCEQ, and LCS, exploring their respective coupled coordination relationships with SCP. This clarifies the impact mechanism between SCP and LCL, providing a theoretical basis for smart low-carbon city co-development. The limitations of the study are also appreciated. Firstly, the study only selected a sample of cities in China, and the limited number of samples may not fully substantiate the research conclusions. Secondly, the indicator system constructed by this study is still not perfect, leading to certain inaccuracies in the evaluation results. In this regard, future studies are recommended to conduct a more comprehensive comparison analysis on the coupled coordination relationship between SCP and LCL at city, regional, and national levels, which would be beneficial in better guiding the practice of urban sustainability.

Data availability

All data generated or analysed during this study are included in this published article [and its Supplementary Information files].

Zheng, H. W., Shen, G. Q. & Wang, H. A review of recent studies on sustainable urban renewal. Habit. Int. 41 , 272–279. https://doi.org/10.1016/j.habitatint.2013.08.006 (2014).

Article CAS Google Scholar

Bibri, S. E. & Krogstie, J. Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustain. Cities Soc. 31 , 183–212. https://doi.org/10.1016/j.scs.2017.02.016 (2017).

Article Google Scholar

Chen, M., Liu, W. & Lu, D. Challenges and the way forward in China’s new-type urbanization. Land Use Policy 55 , 334–339. https://doi.org/10.1016/j.landusepol.2015.07.025 (2016).

Liang, W. & Yang, M. Urbanization, economic growth and environmental pollution: Evidence from China. Sustain. Comput. Inform. Syst. 21 , 1–9. https://doi.org/10.1016/j.suscom.2018.11.007 (2019).

Guan, X., Wei, H., Lu, S., Dai, Q. & Su, H. Assessment on the urbanization strategy in China: Achievements, challenges and reflections. Habit. Int. 71 , 97–109. https://doi.org/10.1016/j.habitatint.2017.11.009 (2018).

Wu, H., Hao, Y. & Weng, J.-H. How does energy consumption affect China’s urbanization? New evidence from dynamic threshold panel models. Energy Policy 127 , 24–38. https://doi.org/10.1016/j.enpol.2018.11.057 (2019).

Liu, H., Cui, W. & Zhang, M. Exploring the causal relationship between urbanization and air pollution: Evidence from China. Sustain. Cities Soc. 80 , 783. https://doi.org/10.1016/j.scs.2022.103783 (2022).

Tang, F. et al. Spatio-temporal variation and coupling coordination relationship between urbanisation and habitat quality in the Grand Canal, China. Land Use Policy 117 , 6119. https://doi.org/10.1016/j.landusepol.2022.106119 (2022).

Kim, J. Smart city trends: A focus on 5 countries and 15 companies. Cities 123 , 551. https://doi.org/10.1016/j.cities.2021.103551 (2022).

Silva, B. N., Khan, M. & Han, K. Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustain. Cities Soc. 38 , 697–713. https://doi.org/10.1016/j.scs.2018.01.053 (2018).

Yigitcanlar, T., Kankanamge, N. & Vella, K. How are smart city concepts and technologies perceived and utilized? A systematic geo-twitter analysis of smart cities in Australia. J. Urban Technol. 28 , 135–154. https://doi.org/10.1080/10630732.2020.1753483 (2021).

Yigitcanlar, T. et al. Can cities become smart without being sustainable? A systematic review of the literature. Sustain. Cities Soc. 45 , 348–365. https://doi.org/10.1016/j.scs.2018.11.033 (2019).

Guo, Q. & Zhong, J. The effect of urban innovation performance of smart city construction policies: Evaluate by using a multiple period difference-in-differences model. Technol. Forecast. Soc. Change 184 , 2003. https://doi.org/10.1016/j.techfore.2022.122003 (2022).

Ismagilova, E., Hughes, L., Dwivedi, Y. K. & Raman, K. R. Smart cities: Advances in research—An information systems perspective. Int. J. Inf. Manag. 47 , 88–100. https://doi.org/10.1016/j.ijinfomgt.2019.01.004 (2019).

Caragliu, A. & Del Bo, C. F. Smart innovative cities: The impact of Smart City policies on urban innovation. Technol. Forecast. Soc. Change 142 , 373–383. https://doi.org/10.1016/j.techfore.2018.07.022 (2019).

Yigitcanlar, T. et al. Understanding ‘smart cities’: Intertwining development drivers with desired outcomes in a multidimensional framework. Cities 81 , 145–160. https://doi.org/10.1016/j.cities.2018.04.003 (2018).

Liu, Z. et al. Decision optimization of low-carbon dual-channel supply chain of auto parts based on smart city architecture. Complexity 2020 , 5951. https://doi.org/10.1155/2020/2145951 (2020).

Guo, Q., Wang, Y. & Dong, X. Effects of smart city construction on energy saving and CO 2 emission reduction: Evidence from China. Appl. Energy 313 , 879. https://doi.org/10.1016/j.apenergy.2022.118879 (2022).

Cheng, J., Yi, J., Dai, S. & Xiong, Y. Can low-carbon city construction facilitate green growth? Evidence from China’s pilot low-carbon city initiative. J. Clean. Prod. 231 , 1158–1170. https://doi.org/10.1016/j.jclepro.2019.05.327 (2019).

Sun, W. & Huang, C. Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency. J. Clean. Prod. 338 , 414. https://doi.org/10.1016/j.jclepro.2022.130414 (2022).

Shi, B., Li, N., Gao, Q. & Li, G. Market incentives, carbon quota allocation and carbon emission reduction: Evidence from China’s carbon trading pilot policy. J. Environ. Manag. 319 , 650. https://doi.org/10.1016/j.jenvman.2022.115650 (2022).

Sun, L. et al. Carbon emission transfer strategies in supply chain with lag time of emission reduction technologies and low-carbon preference of consumers. J. Clean. Prod. 264 , 664. https://doi.org/10.1016/j.jclepro.2020.121664 (2020).

Matsumura, E. M., Prakash, R. & Vera-Munoz, S. C. Firm-value effects of carbon emissions and carbon disclosures. Acc. Rev. 89 , 695–724. https://doi.org/10.2308/accr-50629 (2014).

Lv, M. & Bai, M. Evaluation of China’s carbon emission trading policy from corporate innovation. Financ. Res. Lett. 39 , 565. https://doi.org/10.1016/j.frl.2020.101565 (2021).

Jia, Z. & Lin, B. Rethinking the choice of carbon tax and carbon trading in China. Technol. Forecast. Soc. Change 159 , 187. https://doi.org/10.1016/j.techfore.2020.120187 (2020).

Huo, T., Xu, L., Liu, B., Cai, W. & Feng, W. China’s commercial building carbon emissions toward 2060: An integrated dynamic emission assessment model. Appl. Energy 325 , 828. https://doi.org/10.1016/j.apenergy.2022.119828 (2022).

Lin, B. & Huang, C. Analysis of emission reduction effects of carbon trading: Market mechanism or government intervention? Sustain. Prod. Consump. 33 , 28–37. https://doi.org/10.1016/j.spc.2022.06.016 (2022).

Zhang, M. & Liu, Y. Influence of digital finance and green technology innovation on China’s carbon emission efficiency: Empirical analysis based on spatial metrology. Sci. Total Environ. 838 , 463. https://doi.org/10.1016/j.scitotenv.2022.156463 (2022).

Zhu, X. & Li, D. How to promote the construction of low-carbon cities in China? An urban complex ecosystem perspective. Sustain. Dev. https://doi.org/10.1002/sd.2897 (2024).

He, C., Zhang, D., Huang, Q. & Zhao, Y. Assessing the potential impacts of urban expansion on regional carbon storage by linking the LUSD-urban and InVEST models. Environ. Model. Softw. 75 , 44–58. https://doi.org/10.1016/j.envsoft.2015.09.015 (2016).

Nowak, D. J., Greenfield, E. J., Hoehn, R. E. & Lapoint, E. Carbon storage and sequestration by trees in urban and community areas of the United States. Environ. Pollut. 178 , 229–236. https://doi.org/10.1016/j.envpol.2013.03.019 (2013).

Article CAS PubMed Google Scholar

Wang, T. et al. Mobility based trust evaluation for heterogeneous electric vehicles network in smart cities. IEEE Trans. Intell. Transp. Syst. 22 , 1797–1806. https://doi.org/10.1109/tits.2020.2997377 (2021).

Huovila, A., Bosch, P. & Airaksinen, M. Comparative analysis of standardized indicators for Smart sustainable cities: What indicators and standards to use and when? Cities 89 , 141–153. https://doi.org/10.1016/j.cities.2019.01.029 (2019).

Nizetic, S., Djilali, N., Papadopoulos, A. & Rodrigues, J. J. P. C. Smart technologies for promotion of energy efficiency, utilization of sustainable resources and waste management. J. Clean. Prod. 231 , 565–591. https://doi.org/10.1016/j.jclepro.2019.04.397 (2019).

Sharifi, S., Saman, W. & Alemu, A. Identification of overheating in the top floors of energy-efficient multilevel dwellings. Energy Build. https://doi.org/10.1016/j.enbuild.2019.109452 (2019).

Shafiq, M., Tian, Z., Sun, Y., Du, X. & Guizani, M. Selection of effective machine learning algorithm and Bot–IoT attacks traffic identification for internet of things in smart city. Future Gener. Comput. Syst. Int. J. Esci. 107 , 433–442. https://doi.org/10.1016/j.future.2020.02.017 (2020).

Huang, S., Liu, A., Zhang, S., Wang, T. & Xiong, N. N. BD-VTE: A novel baseline data based verifiable trust evaluation scheme for smart network systems. IEEE Trans. Netw. Sci. Eng. 8 , 2087–2105. https://doi.org/10.1109/tnse.2020.3014455 (2021).

Reed, M. S. et al. Evaluating impact from research: A methodological framework. Res. Policy 50 , 147. https://doi.org/10.1016/j.respol.2020.104147 (2021).

Venable, J., Pries-Heje, J. & Baskerville, R. FEDS: A framework for evaluation in design science research. Eur. J. Inf. Syst. 25 , 77–89. https://doi.org/10.1057/ejis.2014.36 (2016).

Kristan, M. et al. A novel performance evaluation methodology for single-target trackers. IEEE Trans. Pattern Anal. Mach. Intell. 38 , 2137–2155. https://doi.org/10.1109/tpami.2016.2516982 (2016).

Article PubMed Google Scholar

Li, H. Research progress on evaluation methods and factors influencing shale brittleness: A review. Energy Rep. 8 , 4344–4358. https://doi.org/10.1016/j.egyr.2022.03.120 (2022).

Lyu, H.-M., Zhou, W.-H., Shen, S.-L. & Zhou, A.-N. Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen. Sustain. Cities Soc. 56 , 103. https://doi.org/10.1016/j.scs.2020.102103 (2020).

Buyukozkan, G. & Guleryuz, S. An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey. Int. J. Prod. Econ. 182 , 435–448. https://doi.org/10.1016/j.ijpe.2016.09.015 (2016).

Ervural, B. C., Zaim, S., Demirel, O. F., Aydin, Z. & Delen, D. An ANP and fuzzy TOPSIS-based SWOT analysis for Turkey’s energy planning. Renew. Sustain. Energy Rev. 82 , 1538–1550. https://doi.org/10.1016/j.rser.2017.06.095 (2018).

Gao, Z. et al. EEG-based spatio-temporal convolutional neural network for driver fatigue evaluation. IEEE Trans. Neural Netw. Learn. Syst. 30 , 2755–2763. https://doi.org/10.1109/tnnls.2018.2886414 (2019).

Manzano, A. The craft of interviewing in realist evaluation. Evaluation 22 , 342–360. https://doi.org/10.1177/1356389016638615 (2016).

Zeng, S., Jin, G., Tan, K. & Liu, X. Can low-carbon city construction reduce carbon intensity? Empirical evidence from low-carbon city pilot policy in China. J. Environ. Manag. 332 , 363. https://doi.org/10.1016/j.jenvman.2023.117363 (2023).

Liu, X., Li, Y., Chen, X. & Liu, J. Evaluation of low carbon city pilot policy effect on carbon abatement in China: An empirical evidence based on time-varying DID model. Cities 123 , 582. https://doi.org/10.1016/j.cities.2022.103582 (2022).

Tan, S. et al. A holistic low carbon city indicator framework for sustainable development. Appl. Energy 185 , 1919–1930. https://doi.org/10.1016/j.apenergy.2016.03.041 (2017).

Article ADS Google Scholar

Shi, X. & Xu, Y. Evaluation of China’s pilot low-carbon city program: A perspective of industrial carbon emission efficiency. Atmos. Pollut. Res. 13 , 446. https://doi.org/10.1016/j.apr.2022.101446 (2022).

Yang, S., Pan, Y. & Zeng, S. Decision making framework based Fermatean fuzzy integrated weighted distance and TOPSIS for green low-carbon port evaluation. Eng. Appl. Artif. Intell. 114 , 5048. https://doi.org/10.1016/j.engappai.2022.105048 (2022).

Fang, G., Gao, Z., Tian, L. & Fu, M. What drives urban carbon emission efficiency?—Spatial analysis based on nighttime light data. Appl. Energy 312 , 772. https://doi.org/10.1016/j.apenergy.2022.118772 (2022).

Yang, S., Jahanger, A. & Hossain, M. R. How effective has the low-carbon city pilot policy been as an environmental intervention in curbing pollution? Evidence from Chinese industrial enterprises. Energy Econ. 118 , 523. https://doi.org/10.1016/j.eneco.2023.106523 (2023).

Huang, G., Li, D., Zhu, X. & Zhu, J. Influencing factors and their influencing mechanisms on urban resilience in China. Sustain. Cities Soc. 74 , 210. https://doi.org/10.1016/j.scs.2021.103210 (2021).

Li, W. et al. Carbon emission and economic development trade-offs for optimizing land-use allocation in the Yangtze River Delta, China. Ecol. Indic. 147 , 950. https://doi.org/10.1016/j.ecolind.2023.109950 (2023).

Wu, H. et al. Exploring the impact of urban form on urban land use efficiency under low-carbon emission constraints: A case study in China’s Yellow River Basin. J. Environ. Manag. 311 , 866. https://doi.org/10.1016/j.jenvman.2022.114866 (2022).

Zhao, S. et al. Has China’s low-carbon strategy pushed forward the digital transformation of manufacturing enterprises? Evidence from the low-carbon city pilot policy. Environ. Impact Assess. Rev. 102 , 184. https://doi.org/10.1016/j.eiar.2023.107184 (2023).

Pan, A., Zhang, W., Shi, X. & Dai, L. Climate policy and low-carbon innovation: Evidence from low-carbon city pilots in China. Energy Econ. 112 , 129. https://doi.org/10.1016/j.eneco.2022.106129 (2022).

De Jong, M., Joss, S., Schraven, D., Zhan, C. & Weijnen, M. Sustainable-smart-resilient-low carbon-eco-knowledge cities; making sense of a multitude of concepts promoting sustainable urbanization. J. Clean. Prod. 109 , 25–38. https://doi.org/10.1016/j.jclepro.2015.02.004 (2015).

He, B.-J. et al. Co-benefits approach: Opportunities for implementing sponge city and urban heat island mitigation. Land Use Policy 86 , 147–157. https://doi.org/10.1016/j.landusepol.2019.05.003 (2019).

Nizetic, S., Solic, P., Lopez-de-Ipina, D. & Patrono, L. Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future. J. Clean. Prod. 274 , 877. https://doi.org/10.1016/j.jclepro.2020.122877 (2020).

Abduljabbar, R. L., Liyanage, S. & Dia, H. The role of micro-mobility in shaping sustainable cities: A systematic literature review. Transp. Res. D Transp. Environ. 92 , 734. https://doi.org/10.1016/j.trd.2021.102734 (2021).

Anh Tuan, H., Van Viet, P. & Xuan Phuong, N. Integrating renewable sources into energy system for smart city as a sagacious strategy towards clean and sustainable process. J. Clean. Prod. 305 , 7161. https://doi.org/10.1016/j.jclepro.2021.127161 (2021).

March, H. & Ribera-Fumaz, R. Smart contradictions: The politics of making Barcelona a self-sufficient city. Eur. Urban Reg. Stud. 23 , 816–830. https://doi.org/10.1177/0969776414554488 (2016).

Yigitcanlar, T. & Lee, S. H. Korean ubiquitous-eco-city: A smart-sustainable urban form or a branding hoax? Technol. Forecast. Soc. Change 89 , 100–114. https://doi.org/10.1016/j.techfore.2013.08.034 (2014).

Kumar, S., Sharma, D., Rao, S., Lim, W. M. & Mangla, S. K. Past, present, and future of sustainable finance: Insights from big data analytics through machine learning of scholarly research. Ann. Oper. Res. https://doi.org/10.1007/s10479-021-04410-8 (2022).

Article PubMed PubMed Central Google Scholar

Li, Y., Gao, P., Tang, B., Yi, Y. & Zhang, J. Double feature extraction method of ship-radiated noise signal based on slope entropy and permutation entropy. Entropy 24 , 22. https://doi.org/10.3390/e24010022 (2022).

Article ADS CAS Google Scholar

Zavadskas, E. K., Mardani, A., Turskis, Z., Jusoh, A. & Nor, K. M. D. Development of TOPSIS method to solve complicated decision-making problems: An overview on developments from 2000 to 2015. Int. J. Inf. Technol. Decis. Making 15 , 645–682. https://doi.org/10.1142/s0219622016300019 (2016).

Edelmann, D., Mori, T. F. & Szekely, G. J. On relationships between the Pearson and the distance correlation coefficients. Stat. Probab. Lett. 169 , 960. https://doi.org/10.1016/j.spl.2020.108960 (2021).

Article MathSciNet Google Scholar

Wang, S., Kong, W., Ren, L., Zhi, D. & Dai, B. Research on misuses and modification of coupling coordination degree model in China. J. Nat. Resour. 36 , 793–810 (2021).

CAS Google Scholar

Baak, M., Koopman, R., Snoek, H. & Klous, S. A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics. Comput. Stat. Data Anal. 152 , 7043. https://doi.org/10.1016/j.csda.2020.107043 (2020).

Saadatmorad, M., Talookolaei, R.-A.J., Pashaei, M.-H., Khatir, S. & Wahab, M. A. Pearson correlation and discrete wavelet transform for crack identification in steel beams. Mathematics 10 , 689. https://doi.org/10.3390/math10152689 (2022).

De Winter, J. C. F., Gosling, S. D. & Potter, J. Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychol. Methods 21 , 273–290. https://doi.org/10.1037/met0000079 (2016).

Belz, F. M. & Binder, J. K. Sustainable entrepreneurship: A convergent process model. Bus. Strategy Environ. 26 , 1–17. https://doi.org/10.1002/bse.1887 (2017).

Download references

The funding provided by Government of Jiangsu Province (BE2022606 and BM2022035).

Author information

Authors and affiliations.

Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, 210018, China

Xiongwei Zhu, Dezhi Li, Shenghua Zhou & Lugang Yu

Engineering Research Center of Building Equipment, Energy, and Environment, Ministry of Education, Southeast University, Nanjing, 210018, China

Department of Wood Science, The University of British Columbia, Vancouver, V6T 1Z4, Canada

You can also search for this author in PubMed Google Scholar

Contributions

X.Z: conceptualization, methodology, formal analysis, investigation, writing-original draft. D. L: supervision, project administration, funding acquisition. S. Z: writing-review & editing. S.Z: writing-review & editing. L.Y: data curation.

Corresponding author

Correspondence to Dezhi Li .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary tables., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Zhu, X., Li, D., Zhou, S. et al. Evaluating coupling coordination between urban smart performance and low-carbon level in China’s pilot cities with mixed methods. Sci Rep 14 , 20461 (2024). https://doi.org/10.1038/s41598-024-68417-4

Download citation

Received : 28 March 2024

Accepted : 23 July 2024

Published : 03 September 2024

DOI : https://doi.org/10.1038/s41598-024-68417-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Smart city performance (SCP)
Low-carbon level (LCL)
ETPC analysis model
Coupling coordination degree
Smart low-carbon coupling coordination development paths

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

IMAGES

A-Level Economics: Model Essays in Microeconomics
A-Level Economics: Model Essays in Macroeconomics
A Level Economics Essays [20/25 markers]
A-Level Economics: Model Essays in Microeconomics Second Edition
A-Level Economics Model Essays and Case-Study Answers in Macroeconomics
A-Level Economics Theme 1 model essays (A* level) (Suitable for 2024

VIDEO

[A level Economics] How to Improve Your Econs Essays
jkpsc assistant director || how to prepare || minimum resources || booklist for compulsory papers
ESSAY PRACTICE: Factors Affecting Attraction
A-Level Economics
CIE A2 Economics
Basic Economic Problem & Opportunity Cost

COMMENTS

50+ FREE Economics Essay Questions and Answers │ A-level
Market Structures and Competition Essays. CIE and AQA economics model essays for A level, AS level, GCSE, IGCSE and O level. All questions are taken from past papers and Solutions are included. Relevant for years 10, 11, 12 and 13. Questions and answers are classified by topic.
MODEL Economics Essays │ Questions and Answers by Topic
And with the option to download economics essays in PDF format, learning on-the-go has never been easier. CIE and AQA economics model essays for A level, AS level, GCSE, IGCSE and O level. All questions are taken from past papers and Solutions are included. Relevant for years 10, 11, 12 and 13. Questions and answers are classified by topic.
A-Level Economics Model Answers (Edexcel)
Download A-Level Economics Model Answers for Edexcel past papers below for A2 and AS. These candidate responses were extracted from Edexcel exam board's examiners' reports and were graded by Edexcel examiners. All new specification Economics papers and their model answers are now available including Paper 1 (Microeconomics), Paper 2 ...
Economics : Model Essays (Second Edition)
Economics: Model Essays is the 2nd edition of the best-selling revision guide that helps students gain a strong grade in the essay component of the 2023-2025 Cambridge International AS & A Level Economics (9708) examination paper. With over 50 new model essays, the book provides a clear essay structure for each essay, various tips, and conceptual diagrams on basic economics concepts to give ...
Economics Model Essays
Economics Model Essay 2. After reaching a rate of 8.3 percent in 2010, GDP growth in Asia is projected to average nearly 7 percent in both 2011 and 2012, according to the IMF. However, due to the growing unrest in the Middle East sparked by the Egyptian Revolution that began on 25 January 2011, oil prices have started to rise.
Economics Revision Essay Plans
Economics Revision Essay Plans. This series of resources provides revision essay plans for a wide variety of essay topics, including synoptic questions. For the 2019 papers check out our collection of videos on building A* evaluation into your answers. Have you tried our series of more than 50 Quizlet revision activities?
A Level Econs Model Essay: Macroeconomic Aims, Issues And Policies
Recent macroeconomic problems may or may not be attributed to changes in the value of the exchange rate. In recent years, Singapore had to tackle the twin threats of inflation and a global economic slowdown. The root causes of such problems are largely external in nature and cannot be attributed to a weakening of the Sing dollar.
A-Level Economics A* model essays for All Themes (Suitable for 2024
When it comes down to writing essays for A-Level Economics it can get a bit tricky. This resource is packed with helpful guidance for you to succeed, ranging from model essays on key topic areas to structures for a top essay. I did all of this and in the 2022 exams I achieved 88% (295/335) in my actual A-Level, a comfortable A* by over 10%!
Free Economics Essays and Papers
Here is a step by step guide that will help you write a striking A* economics essay! Part one: Reading the exam question. Step 1: Underline the content words. Step 2: Underline Command words. Step 3: Look for extra requirements. Step 4: Look at the number of marks. Step 5: Plan time management. Part 2: Writing your essay.
A-Level Economics Resources Overview: Resources and Articles
Edexcel Economics A style model answers and skills. Edexcel A essay structures for each essay.; 25 marker model answer [tariffs].; 25 marker model answer [supply-side policies].; 15 marker model answer [tradable pollution permits].; 12 marker model answer [fall in private sector investment].; 8 marker model answer [externalities in coal].; Edexcel Economics A Paper 1 style current affairs ...
Tips for writing economics essays
Some tips for writing economics essays Includes how to answer the question, including right diagrams and evaluation - primarily designed for A Level students. 1. Understand the question. Make sure you understand the essential point of the question. If appropriate, you could try and rephrase the question into a simpler version.
Example 25-Mark Essay in style of AQA Economics A-level
Question for model answer. Consider the following question. I have written this question in the style of a 25-mark AQA Economics A-level question for section B: Taking effect from 1st April 2023, the UK Government has committed to increasing the corporation tax rate from 19% to 25% for companies with profits above £250,000 per year.
Economics: Model Essays Paperback
Tailored to prepare students for Paper 2 and Paper 4 of the 2019-2021 Cambridge International AS & A Level Economics (9708) examination. The model essays act as revision guides and are specially written as answers to selected past year questions, supported by essay outlines and notes on how students can successfully compose convincing essays.
How To Write A Good Economics Essay (IB & JC A Level)? By The Economics
Step 4: Body of Essay. In the body, there will be several paragraphs. The number of points/paragraphs depends on the question. It is common to require 2 main points for each 10 mark essay and similarly for 15 mark essay questions. Under each main point, there may be 1-2 sub-points.
Economics : Model Essays
Economics: Model Essays is tailored to prepare students for Paper 2 and Paper 4 of the 2019-2021 Cambridge International AS & A Level Economics (9708) examination. The model essays are specially written as answers to selected past year questions, and are supported with essay outlines and notes on how students can successfully compose convincing essays.
2018 Practice Essays for A Level Economics
Micro Essays. Housing. Home ownership has become increasingly difficult to access, particularly for first-time buyers, as house price growth has outstripped growth in wages. Median house prices in England are now 7.7 times higher than median earnings. In London, the ratio can be considerably higher: in Chelsea & Fulham, it is 24.8.
A-Levels H1-H2 Econs Model Essays
Microeconomics Essay Sample Answer. 2. Macroeconomics Essay Sample Answer . Common Mistakes Students make in Economics Essay Writing. Many students do revise and study the lecture notes, and attempt to do their essay writing. However, this approach leads itself into several common mistakes, namely: 1) Write EXCESSIVELY on the basic content answers.
A-Level Economics Essays
A-Level Economics Essays. £ 9.00. 50 A-Level economic essays and model answers. Comprehensive answers which illustrate - how to answer the question, how to effectively evaluate and get a high A grade. Comes in e-book, pdf format ( Sent via email straight after purchase.) All questions are taken from past exam papers.
AS Economics Model Essays
The essays were all written exclusively for this pdf guide. List of 41 questions included in the pdf at the bottom. The cost for AS Level Essays is only £8.00. View: sample of AS essays. Using the Model Essays. A good way to use this guide is to take a question and write down an essay plan of what you would write for the question.
A-Level Economics Theme 4 model essays (A* level) (Suitable for 2024
pdf, 478.21 KB. Want to ace A-Level Economics Theme 4 Macroeconomics? Then these A* level model essays are perfect for you! When it comes down to writing essays for A-Level Economics it can get a bit tricky. This resource is packed with helpful guidance for you to succeed, ranging from model essays on key topic areas to structures for a top essay.
9708 AS Economics F/M/2023 Model Essay 2(a) : r/alevel
Want to ace your A-Level economics exams? We've compiled perfect model essays and formulas to help you score big! 💯⭐️ Gain insider tips to make your essays stand out. 💡 ️ Don't hesitate, master these precious exam secrets now! Model Essay 1 Model Essay 2. Model Essay 3. go appstore in iphone orgoogleplay to download All As.
Edexcel Economics (A) A Level: 2024 Microeconomics Topic Tracker
Here's our latest Edexcel Economics (A) A level microeconomics topic tracker, fully updated with the summer 2024 exams. Here's our latest Edexcel Economics (A) A level microeconomics topic tracker, fully updated with the summer 2024 exams. ... Essay on Oligopoly and Collusion Exam Support. Key Topics to Revise for Paper 1 Micro in 2019 Exam ...
Three essays on enviromental and economic sustainability of ...
In this context, Chapter 2 makes a first approach to the analysis of the potential impacts of this reform at the interindustrial level on gross added value; the pulling effects between industries; and the impacts on final demand. The analysis is carried out with an input-output model, five different scenarios are simulated.
Evaluating coupling coordination between urban smart ...
The coupling coordination degree characterizes the level of interaction between different systems and serves as a scientific model for measuring the coordinated development level of multiple ...