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Thesis Topics
The dissertation projects of the DK (in the first phase from 2014 to 2018) contribute to finding answers to three questions:
- How do we understand and deal with climate change uncertainties in the natural and social sciences as well as from the perspective of normative theories?
- What are critical thresholds of environmental, social and economic systems considering their vulnerability and how are these thresholds related to the normative threshold of sufficiency, that is, the threshold of well-being below which persons’ basic rights are infringed or violated?
- What are scientifically sound, technologically and institutionally feasible, economically efficient, and ethically defensible and sustainable strategies to cope with climate change, particularly taking into account the problems of implementation in an environment characterized by uncertainties and thresholds?
Phd projects dealing with research question 1
student | dissertation project | supervisor co-supervisor |
Lukas Brunner | Uncertainties in atmospheric circulation processes at mid-latitudes during recent climate change | Steiner Birk |
Kian Mintz-Woo | Moral Uncertainty about Climate Change: What is it, Does it Matter, and How? | Meyer Steininger |
Sungmin O | Uncertainties in measured extreme precipitation events | Foelsche Sass |
Katharina Schröer | Exploring the causes of rare extreme precipitation events in the south-eastern Alpine forelands | Kirchengast Sass |
Josef Innerkofler | Radio occultation excess phase processing with integrated uncertainty estimation and use for tracing climate change signals | Kirchengast Birk |
Hallgeir Wilhelmsen | Climate change diagnostics from atmospheric observations and climate model data | Steiner Winiwarter |
Phd projects dealing with research question 2
student | dissertation project | supervisor, co-supervisor |
Sajeev Erangu Purath Mohankumar | Scenarios of low carbon society—sector agriculture | Winiwarter, Steininger |
Johannes Haas | Impact of climate change on groundwater resources: Feedback mechanisms and thresholds unter drought conditions | Birk, Posch |
Clara Hohmann | Uncertainties and thresholds of hydrological changes in south-eastern Austria in a warming climate | Kirchengast, Birk |
Michael Kriechbaum | Social and economic uncertainties and thresholds for the diffusion and adoption of renewable energy systems | Posch, Bednar-Friedl |
Florian Ortner | Integrative Perspectives of Natural Hazards in Alpine Valleys | Sass, Steininger |
Silke Carmen Lutzmann | Thresholds in torrential systems of alpine watersheds | Sass, Foelsche |
Eike Düvel | The Normative Significance of the Imposition of Risks of Rights Violations in the Context of Climate Change | Meyer, Baumgartner |
Phd projects dealing with research question 3
student | dissertation project | supervisor, co-supervisor |
Matthias Damert | Individual mobility as climate challenge—Climate change risks and corporate vulnerability in the automotive sector | Baumgartner, Bednar-Friedl |
Javier Lopez Pról | Transformation to a Low Carbon Economy | Steininger, Posch |
Yadira Mori-Clement | Coping with climate change: fair burden sharing among industrialized and developing countries | Bednar-Friedl, Meyer |
Arijit Paul | Sustainable strategies of companies in energy intensive sectors to cope with climate change | Baumgartner, Meyer |
Christian Unterberger | Thresholds and fat tail risks in public decision making about climate change | Steininger, Kirchengast |
Daniel Petz | Sufficientarian Weighing of the Imposition of Risks of Rights Violations and Other Set-backs of Interest in the Context of Climate Change | Meyer, Winiwarter |
Vincent Hess | Economic and Ethical Consequences of Natural Hazards in Alpine Valleys | Steininger, Sass |
Philipp Babcicky | Private Adaptation to Climate Change: Explaining Adaptive Behaviour of Flood-prone Households | Posch, Steiner |
Hannah Hennighausen | Understanding the effects of risk, uncertainty and externalities on decision-making in the context of climate change adaptation | Bednar-Friedl, Foelsche |
Stefan Nabernegg | Instruments for GHG emission reductions: A macroeconomic evaluation of technological, regulative and behavioral policies | Bednar-Friedl, Baumgartner |
- Coordinators
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- Summer School "Coping with Climate Change"
- Winter School "Climate Change Thresholds"
- World Symposium "Climate Change Communication"
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Analysis of wheat-yield prediction using machine learning models under climate change scenarios.
1. Introduction
- Identifying the key factors influencing wheat production
- Modeling and testing wheat-yield responses to rainfall and temperature variables using various methods such as boosting tree, ANNs, random forest regression, multiple linear regression, and ensemble models, based on observed yield and climatic data
- Anticipating and analyzing the potential influence of climate change on wheat crop trends up to the year 2052.
2. Materials and Methods
2.1. research workflow, 2.2. study area, 2.3. observed data, 2.4. gcm data, 2.5. data processing, data splitting, 2.6. experimental setup, 3. machine learning algorithms, 3.1. multiple linear regression, 3.2. xgboost, 3.3. random forest regression, 3.4. artificial neural networks (anns) model, 3.5. ensemble model, 3.6. evaluation metrics.
- Conduct a correlation analysis between climate factors and wheat yields and identify the climate variables most significantly correlated with wheat production through statistical hypothesis testing of relationships.
- Apply various machine learning models (RF, MLR, boosting tree, MLP, PNN, GFF) to the historical climate and wheat-yield data to generate predictions and compare against actual historical yields.
- Evaluate the various machine learning models using training performance metrics such as R 2 , R, nRMSE, MAE, MBE, and RMSE to identify the best performers.
- To downscale coarse resolution GCM climate projections to local scales under three emission scenarios (SRA1B, SRB1, SRA2) using the XGboost statistical downscaling model.
- Applying the best-performing machine learning model to project wheat yields over periods for three locations and emission scenarios using the downscaled generated climate variables data.
4.1. Importance of the Climate Parameters on Wheat Yield
4.2. selection of predictors and predicted variables, 4.3. performance metrics of different mla, 4.4. downscaling climate projections using the xgboost algorithm, 4.5. wheat-yield prediction over 2052, 5. comparison of the proposed method with existing techniques, 6. discussion.
- More weather stations are needed across Pakistan to thoroughly examine nationwide climate-agriculture relationships at the district and provincial levels.
- Developing and promoting wheat varieties that exhibit heat tolerance, drought resistance, and disease resilience is crucial. These climate-smart cultivars can withstand extreme temperatures and water scarcity, ensuring stable yields.
- Early sowing and climate-informed planting dates help avoid extreme heat stress during grain filling. Adjusting planting windows based on weather forecasts improves yield outcomes.
- Efficient irrigation systems, rainwater harvesting, and proper water scheduling are essential. Consistent water supply during critical growth stages enhances yield.
- Mapping suitable crop habitats can aid precautionary measures and innovations to boost agricultural output and food security amid climate shifts.
7. Conclusions
Author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
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Click here to enlarge figure
Locations | Variables | Statistics |
---|
Average | Minimum | Maximum | Standard Deviation | SE | Skewness | CL |
---|
Okara | Rainfall | 500.65 | 301.78 | 836.2 | 139.57 | 25.07 | 0.540 | 27.87 |
| 32.48 | 30.83 | 33.83 | 0.818 | 0.147 | 0.2794 | 2.520 |
| 21.00 | 19.50 | 22.25 | 0.618 | 0.11 | 0.132 | 2.946 |
Yield | 3.428 | 2.5006 | 3.904 | 0.416 | 0.074 | −0.878 | 12.15 |
Sargodha | Rainfall | 725.62 | 477.30 | 1131.7 | 173.52 | 31.165 | 0.511 | 23.91 |
| 31.59 | 29.83 | 33.16 | 0.860 | 0.154 | 0.123 | 2.723 |
| 20.59 | 18.25 | 21.91 | 0.965 | 0.173 | −1.13 | 4.689 |
Yield | 2.613 | 2.194 | 2.994 | 0.259 | 0.046 | −0.270 | 9.926 |
Multan | Rainfall | 337.68 | 169.30 | 553.8 | 114.94 | 20.64 | 0.089 | 34.04 |
| 33.02 | 31.33 | 35.00 | 1.019 | 0.183 | 0.3685 | 3.086 |
| 21.74 | 20.08 | 23.41 | 0.902 | 0.162 | −0.389 | 4.14 |
Yield | 2.822 | 1.996 | 3.647 | 0.456 | 0.0820 | 0.020 | 16.18 |
All sites | Rainfall | 521.31 | 169.3 | 1131.7 | 214.59 | 22.25 | 0.550 | 41.16 |
| 32.36 | 29.83 | 35.0 | 1.07 | 0.11 | 0.263 | 3.313 |
| 21.11 | 18.25 | 23.41 | 0.96 | 0.099 | −0.434 | 4.555 |
Yield | 2.954 | 1.99 | 3.904 | 0.517 | 0.0536 | 0.2281 | 17.50 |
Location | Variables | | Covariance | SE | R | t (Value) | p (Value) | 95% Confidence Level |
---|
| | | | | | | | Lower | Upper |
---|
Okara | Rainfall | −0.0001 | 0.000 | 0.006 | 0.1842 | −0.104 | 0.9179 | −0.001 | 0.001 |
| −0.0629 | 0.010 | 0.101 | | −0.620 | 0.540 | −0.271 | 0.145 |
| 0.114 | 0.017 | 0.131 | | 0.869 | 0.3921 | 0.155 | 0.383 |
Sargodha | Rainfall | 0.0001 | 0.000 | 0.0003 | 0.7106 | 0.2646 | 0.793 | −0.0004 | 0.0005 |
| 0.084 | 0.002 | 0.0517 | | 1.627 | 0.115 | −0.021 | 0.190 |
| 0.154 | 0.001 | 0.040 | | 3.869 | 0.0006 | 0.072 | 0.236 |
Multan | Rainfall | 0.0003 | 0.0000 | 0.0006 | 0.6976 | 0.5365 | 0.5960 | −0.0008 | 0.0014 |
| 0.1185 | 0.007 | 0.086 | | 1.3698 | 0.1820 | −0.058 | 0.2958 |
| 0.2482 | 0.009 | 0.0965 | | 2.5722 | 0.0159 | 0.050 | 0.446 |
All sites | Rainfall | 0.000 | 0.000 | 0.0002 | 0.149 | 0.310 | 0.757 | −0.000 | 0.001 |
| 0.116 | 0.004 | 0.066 | | 1.766 | 0.081 | −0.015 | 0.248 |
| 0.114 | 0.004 | 0.066 | | 1.734 | 0.086 | −0.071 | 0.246 |
Rank | Model | MAE | RMSE | nRMSE % | MBE | R | |
---|
1 | ANN(LR) | 0.305 | 0.361 | 24.3 | 0.013 | 0.746 | 0.446 |
2 | ANN(GFF) | 0.220 | 0.301 | 19.2 | −0.180 | 0.888 | 0.663 |
3 | ANN(PNN) | 0.422 | 0.466 | 28.4 | −0.190 | 0.659 | 0.321 |
4 | MLR | 0.307 | 0.361 | 24.3 | 0.012 | 0.746 | 0.440 |
5 | Boosting Tree | 0.198 | 0.253 | 20.0 | 0.010 | 0.902 | 0.741 |
6 | ANN(MLP) | 0.230 | 0.266 | 17.0 | −0.049 | 0.902 | 0.739 |
7 | RFR | 0.182 | 0.227 | 18.0 | 0.030 | 0.909 | 0.791 |
8 | Ensemble | 0.099 | 0.107 | 8.0 | 0.022 | 0.988 | 0.953 |
Locations/ Scenarios | Training | Testing | Validation |
---|
RMSE | nRMSE % | MAE | R | RMSE | nRMSE % | MAE | R | RMSE | nRMSE % | MAE | R |
---|
Okara/B1 | | | | | | | | | | | | |
| 0.244 | 0.90 | 0.102 | 0.979 | 0.248 | 0.90 | 0.113 | 0.970 | 0.210 | 0.63 | 0.200 | 0.970 |
| 0.222 | 0.79 | 0.095 | 0.991 | 0.221 | 0.795 | 0.099 | 0.99 | 0.105 | 0.485 | 0.099 | 0.99 |
Rain | 0.387 | 0.134 | 0.04 | 0.924 | 0.350 | 0.134 | 0.061 | 0.924 | 0.095 | 0.244 | 0.017 | 0.924 |
Multan/B1 | | | | | | | | | | | | |
| 0.107 | 0.382 | 0.045 | 0.994 | 0.105 | 0.385 | 0.049 | 0.994 | 0.210 | 0.619 | 0.200 | 0.994 |
| 0.303 | 1.08 | 0.134 | 0.908 | 0.304 | 1.09 | 0.148 | 0.985 | 0.105 | 0.462 | 0.099 | 0.985 |
Rain | 0.387 | 0.159 | 0.025 | 0.923 | 0.358 | 0.159 | 0.030 | 0.923 | 0.067 | 0.228 | 0.016 | 0.920 |
Sargodha/B1 | | | | | | | | | | | | |
| 0.197 | 0.732 | 0.086 | 0.989 | 0.197 | 0.733 | 0.086 | 0.989 | 0.210 | 0.658 | 0.200 | 0.989 |
| 0.240 | 0.827 | 0.098 | 0.987 | 0.238 | 0.827 | 0.119 | 0.942 | 0.105 | 0.496 | 0.099 | 0.997 |
Rain | 0.416 | 0.126 | 0.039 | 0.909 | 0.437 | 0.126 | 0.044 | 0.900 | 0.090 | 0.172 | 0.017 | 0.900 |
Okara/A2 | | | | | | | | | | | | |
| 0.220 | 0.816 | 0.088 | 0.984 | 0.224 | 0.826 | 0.097 | 0.984 | 0.210 | 0.638 | 0.200 | 0.985 |
| 0.247 | 0.884 | 0.103 | 0.985 | 0.246 | 0.884 | 0.107 | 0.986 | 0.105 | 0.486 | 0.090 | 0.985 |
Rain | 0.123 | 0.042 | 0.019 | 0.993 | 0.119 | 0.043 | 0.028 | 0.99 | 0.083 | 0.211 | 0.011 | 0.99 |
Multan/A2 | | | | | | | | | | | | |
| 0.257 | 0.918 | 0.107 | 0.973 | 0.254 | 0.918 | 0.117 | 0.975 | 0.210 | 0.619 | 0.200 | 0.976 |
| 0.226 | 0.810 | 0.095 | 0.944 | 0.227 | 0.810 | 0.105 | 0.983 | 0.105 | 0.462 | 0.099 | 0.983 |
Rain | 0.073 | 0.030 | 0.009 | 0.99 | 0.067 | 0.032 | 0.011 | 0.99 | 0.291 | 0.983 | 0.113 | 0.998 |
Sargodha/A2 | | | | | | | | | | | | |
| 0.214 | 0.794 | 0.09 | 0.985 | 0.214 | 0.794 | 0.096 | 0.986 | 0.210 | 0.658 | 0.200 | 0.986 |
| 0.239 | 0.825 | 0.096 | 0.988 | 0.237 | 0.845 | 0.116 | 0.988 | 0.105 | 0.496 | 0.099 | 0.988 |
Rain | 0.101 | 0.030 | 0.020 | 0.996 | 0.106 | 0.030 | 0.023 | 0.996 | 0.069 | 0.120 | 0.011 | 0.997 |
Okara/A1B | | | | | | | | | | | | |
| 0.294 | 1.08 | 0.121 | 0.966 | 0.300 | 1.09 | 0.135 | 0.966 | 0.210 | 0.638 | 0.200 | 0.966 |
| 0.233 | 0.834 | 0.105 | 0.989 | 0.232 | 0.835 | 0.110 | 0.980 | 0.105 | 0.485 | 0.099 | 0.989 |
Rain | 0.285 | 0.099 | 0.028 | 0.958 | 0.258 | 0.099 | 0.042 | 0.958 | 0.146 | 0.372 | 0.034 | 0.959 |
Multan/A1B | | | | | | | | | | | | |
| 0.272 | 0.972 | 0.117 | 0.970 | 0.269 | 0.972 | 0.127 | 0.970 | 0.210 | 0.619 | 0.200 | 0.970 |
| 0.254 | 0.907 | 0.109 | 0.983 | 0.255 | 0.907 | 0.119 | 0.983 | 0.105 | 0.462 | 0.099 | 0.983 |
Rain | 0.349 | 0.144 | 0.026 | 0.937 | 0.324 | 0.144 | 0.031 | 0.937 | 0.105 | 0.356 | 0.031 | 0.937 |
Sargodha/A1B | | | | | | | | | | | | |
| 0.221 | 0.819 | 0.094 | 0.984 | 0.221 | 0.819 | 0.098 | 0.985 | 0.210 | 0.658 | 0.200 | 0.985 |
| 0.369 | 1.27 | 0.156 | 0.953 | 0.367 | 1.274 | 0.189 | 0.953 | 0.105 | 0.49 | 0.099 | 0.954 |
Rain | 0.411 | 0.124 | 0.070 | 0.917 | 0.43 | 0.124 | 0.081 | 0.917 | 0.069 | 0.119 | 0.009 | 0.918 |
Ref/Year | Data Type | Methodology | Factors | | RMSE |
---|
[ ], 2019 | Remote sensing data | SVM | Multiple variables, Yield | 0.93 | 11.7% |
[ ], 2020 | Meteorological and remote sensing data | LSTM, CNN | Phenology variables (11) Climate variable (9) Yield | 0.77 | 721 kg/ha |
[ ], 2020 | Satellite images climate data, soil maps Historical yield | AdaBoost model | Vegetation indices , , mean soil, 6 other features | 0.86 | 0.51 |
[ ], 2021 | Climate and geographical data | SVM | , , mean humidity, min-max WS, Yield | 0.33 | 760kg/ha |
[ ], 2021 | Climate, satellite data soil data | LSTM | , precipitation, Yield soil depth/texture, pH. | 0.83 | 561kg/ha |
[ ], 2022 | Multi source data | RFR | SVI, Climate data, Soil properties, Yield | 0.74 | 758 kg/ha |
[ ], 2022 | Climate, GCM(CMIP6) data | Ensemble Model | Temperature Precipitation SPEI, Yield | 0.705–0.918 | 0.358–0.390 |
[ ], 2023 | Multi-sensor data | Ensemble model | , Sunshine duration Precipitation, Irrigation volume | 0.692 | 0.916 t/ha |
[ ], 2023 | Climate data and Remote sensing, SPEI | SVM | , humidity, RH, WS 2-scenarios wheat | 0.78 | 2.07 |
[ ], 2023 | in-situ, meteorological, and remote sensing | MLR | Multi-variables, Yield | 0.64 | 733.53 kg/ha |
[ ], 2023 | Multi-source data | XGBoost | LST, NDVI pH, 6 other features, Yield | 0.89 | 0.3 |
| | | , , | | |
| The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
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Iqbal, N.; Shahzad, M.U.; Sherif, E.-S.M.; Tariq, M.U.; Rashid, J.; Le, T.-V.; Ghani, A. Analysis of Wheat-Yield Prediction Using Machine Learning Models under Climate Change Scenarios. Sustainability 2024 , 16 , 6976. https://doi.org/10.3390/su16166976
Iqbal N, Shahzad MU, Sherif E-SM, Tariq MU, Rashid J, Le T-V, Ghani A. Analysis of Wheat-Yield Prediction Using Machine Learning Models under Climate Change Scenarios. Sustainability . 2024; 16(16):6976. https://doi.org/10.3390/su16166976
Iqbal, Nida, Muhammad Umair Shahzad, El-Sayed M. Sherif, Muhammad Usman Tariq, Javed Rashid, Tuan-Vinh Le, and Anwar Ghani. 2024. "Analysis of Wheat-Yield Prediction Using Machine Learning Models under Climate Change Scenarios" Sustainability 16, no. 16: 6976. https://doi.org/10.3390/su16166976
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Climate Change: Evidence and Causes: Update 2020 (2020)
Chapter: conclusion, c onclusion.
This document explains that there are well-understood physical mechanisms by which changes in the amounts of greenhouse gases cause climate changes. It discusses the evidence that the concentrations of these gases in the atmosphere have increased and are still increasing rapidly, that climate change is occurring, and that most of the recent change is almost certainly due to emissions of greenhouse gases caused by human activities. Further climate change is inevitable; if emissions of greenhouse gases continue unabated, future changes will substantially exceed those that have occurred so far. There remains a range of estimates of the magnitude and regional expression of future change, but increases in the extremes of climate that can adversely affect natural ecosystems and human activities and infrastructure are expected.
Citizens and governments can choose among several options (or a mixture of those options) in response to this information: they can change their pattern of energy production and usage in order to limit emissions of greenhouse gases and hence the magnitude of climate changes; they can wait for changes to occur and accept the losses, damage, and suffering that arise; they can adapt to actual and expected changes as much as possible; or they can seek as yet unproven “geoengineering” solutions to counteract some of the climate changes that would otherwise occur. Each of these options has risks, attractions and costs, and what is actually done may be a mixture of these different options. Different nations and communities will vary in their vulnerability and their capacity to adapt. There is an important debate to be had about choices among these options, to decide what is best for each group or nation, and most importantly for the global population as a whole. The options have to be discussed at a global scale because in many cases those communities that are most vulnerable control few of the emissions, either past or future. Our description of the science of climate change, with both its facts and its uncertainties, is offered as a basis to inform that policy debate.
A CKNOWLEDGEMENTS
The following individuals served as the primary writing team for the 2014 and 2020 editions of this document:
- Eric Wolff FRS, (UK lead), University of Cambridge
- Inez Fung (NAS, US lead), University of California, Berkeley
- Brian Hoskins FRS, Grantham Institute for Climate Change
- John F.B. Mitchell FRS, UK Met Office
- Tim Palmer FRS, University of Oxford
- Benjamin Santer (NAS), Lawrence Livermore National Laboratory
- John Shepherd FRS, University of Southampton
- Keith Shine FRS, University of Reading.
- Susan Solomon (NAS), Massachusetts Institute of Technology
- Kevin Trenberth, National Center for Atmospheric Research
- John Walsh, University of Alaska, Fairbanks
- Don Wuebbles, University of Illinois
Staff support for the 2020 revision was provided by Richard Walker, Amanda Purcell, Nancy Huddleston, and Michael Hudson. We offer special thanks to Rebecca Lindsey and NOAA Climate.gov for providing data and figure updates.
The following individuals served as reviewers of the 2014 document in accordance with procedures approved by the Royal Society and the National Academy of Sciences:
- Richard Alley (NAS), Department of Geosciences, Pennsylvania State University
- Alec Broers FRS, Former President of the Royal Academy of Engineering
- Harry Elderfield FRS, Department of Earth Sciences, University of Cambridge
- Joanna Haigh FRS, Professor of Atmospheric Physics, Imperial College London
- Isaac Held (NAS), NOAA Geophysical Fluid Dynamics Laboratory
- John Kutzbach (NAS), Center for Climatic Research, University of Wisconsin
- Jerry Meehl, Senior Scientist, National Center for Atmospheric Research
- John Pendry FRS, Imperial College London
- John Pyle FRS, Department of Chemistry, University of Cambridge
- Gavin Schmidt, NASA Goddard Space Flight Center
- Emily Shuckburgh, British Antarctic Survey
- Gabrielle Walker, Journalist
- Andrew Watson FRS, University of East Anglia
The Support for the 2014 Edition was provided by NAS Endowment Funds. We offer sincere thanks to the Ralph J. and Carol M. Cicerone Endowment for NAS Missions for supporting the production of this 2020 Edition.
F OR FURTHER READING
For more detailed discussion of the topics addressed in this document (including references to the underlying original research), see:
- Intergovernmental Panel on Climate Change (IPCC), 2019: Special Report on the Ocean and Cryosphere in a Changing Climate [ https://www.ipcc.ch/srocc ]
- National Academies of Sciences, Engineering, and Medicine (NASEM), 2019: Negative Emissions Technologies and Reliable Sequestration: A Research Agenda [ https://www.nap.edu/catalog/25259 ]
- Royal Society, 2018: Greenhouse gas removal [ https://raeng.org.uk/greenhousegasremoval ]
- U.S. Global Change Research Program (USGCRP), 2018: Fourth National Climate Assessment Volume II: Impacts, Risks, and Adaptation in the United States [ https://nca2018.globalchange.gov ]
- IPCC, 2018: Global Warming of 1.5°C [ https://www.ipcc.ch/sr15 ]
- USGCRP, 2017: Fourth National Climate Assessment Volume I: Climate Science Special Reports [ https://science2017.globalchange.gov ]
- NASEM, 2016: Attribution of Extreme Weather Events in the Context of Climate Change [ https://www.nap.edu/catalog/21852 ]
- IPCC, 2013: Fifth Assessment Report (AR5) Working Group 1. Climate Change 2013: The Physical Science Basis [ https://www.ipcc.ch/report/ar5/wg1 ]
- NRC, 2013: Abrupt Impacts of Climate Change: Anticipating Surprises [ https://www.nap.edu/catalog/18373 ]
- NRC, 2011: Climate Stabilization Targets: Emissions, Concentrations, and Impacts Over Decades to Millennia [ https://www.nap.edu/catalog/12877 ]
- Royal Society 2010: Climate Change: A Summary of the Science [ https://royalsociety.org/topics-policy/publications/2010/climate-change-summary-science ]
- NRC, 2010: America’s Climate Choices: Advancing the Science of Climate Change [ https://www.nap.edu/catalog/12782 ]
Much of the original data underlying the scientific findings discussed here are available at:
- https://data.ucar.edu/
- https://climatedataguide.ucar.edu
- https://iridl.ldeo.columbia.edu
- https://ess-dive.lbl.gov/
- https://www.ncdc.noaa.gov/
- https://www.esrl.noaa.gov/gmd/ccgg/trends/
- http://scrippsco2.ucsd.edu
- http://hahana.soest.hawaii.edu/hot/
| was established to advise the United States on scientific and technical issues when President Lincoln signed a Congressional charter in 1863. The National Research Council, the operating arm of the National Academy of Sciences and the National Academy of Engineering, has issued numerous reports on the causes of and potential responses to climate change. Climate change resources from the National Research Council are available at . |
| is a self-governing Fellowship of many of the world’s most distinguished scientists. Its members are drawn from all areas of science, engineering, and medicine. It is the national academy of science in the UK. The Society’s fundamental purpose, reflected in its founding Charters of the 1660s, is to recognise, promote, and support excellence in science, and to encourage the development and use of science for the benefit of humanity. More information on the Society’s climate change work is available at |
Climate change is one of the defining issues of our time. It is now more certain than ever, based on many lines of evidence, that humans are changing Earth's climate. The Royal Society and the US National Academy of Sciences, with their similar missions to promote the use of science to benefit society and to inform critical policy debates, produced the original Climate Change: Evidence and Causes in 2014. It was written and reviewed by a UK-US team of leading climate scientists. This new edition, prepared by the same author team, has been updated with the most recent climate data and scientific analyses, all of which reinforce our understanding of human-caused climate change.
Scientific information is a vital component for society to make informed decisions about how to reduce the magnitude of climate change and how to adapt to its impacts. This booklet serves as a key reference document for decision makers, policy makers, educators, and others seeking authoritative answers about the current state of climate-change science.
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South Africa has a huge gap between the rich and poor - 4 urgent reasons to tackle inequality
Pro Vice-Chancellor: Climate, Sustainability and Inequality and Director: Southern Centre for Inequality Studies., University of the Witwatersrand
Disclosure statement
Imraan Valodia receives funding from a number of South African and international foundations that support academic and policy research.
University of the Witwatersrand provides support as a hosting partner of The Conversation AFRICA.
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South Africa has exceptionally high levels of inequality . As someone who studies issues of inequality and sustainability, I have argued before that South Africa’s income inequality is the highest of all countries that have data on this. This means that the gap between the rich and the poor is wider than in any other country.
While South Africa is somewhat exceptional, income inequality within countries has been growing across the world.
The most recent data suggests that income inequality between countries has been falling , but this is largely due to the rising incomes of people in China, who make up a large part of the global population.
If we consider inequality in wealth, which gives us a fuller picture than income, the situation in South Africa is even more extreme. The top 0.1% of the population owns 25% of the wealth . Globally, according to the World Inequality Report, the top 10% of the global population owns 76% of the global wealth .
Read more: South Africa can't crack the inequality curse. Why, and what can be done
There are a number of good reasons why the South African government should focus on reducing inequality. I wish to highlight four reasons.
Not good for the economy
First, high levels of inequality are not good for the economy. This is a complex issue, because the causal relationships between economic growth and inequality are multifaceted . But these obscene levels of concentration in wealth leave too much economic power in the hands of a small group of wealthy individuals.
Not good for democracy
Second, high levels of inequality are not good for democracy. Across much of the world, especially in the developed countries, ultra-rightwing politicians such as Donald Trump have been drawing support from the electorate. Among the reasons for this is that working class people feel left behind as wealth and income gaps widen. But in fact, the effect of the economic policies that these right-wing politicians promote is to increase inequality. These political shifts undermine democratic systems, leading to a rise in ultra-nationalism and discrimination against migrants and other minority groups.
We are, unfortunately, seeing the rise of these political views in South Africa too. The rise of this type of politics also undermines multilateral efforts to address global challenges, such as climate change. For example, politicians such as Trump have promoted climate denialism and removed the United States from the Paris Agreement on climate change .
Read more: Inequality: troubling trends and why economic growth in Africa is key to reducing global disparities
Not good for social cohesion
Third, high levels of inequality are not good socially. Not only is inequality bad for social cohesion, it entrenches inter-generational inequalities.
Economist Branko Milanovic , one of the world’s academic authorities on inequality, has shown in his 2016 book that an American child, purely by the chance event of being born in America, is likely to earn 93 times the income of a child who, also by chance, is born in a poor country .
This is especially a problem in South Africa, where a child born in a low-income household is unlikely to go to a good school, and therefore less likely to attend university, and therefore less likely to find employment, and so on. This increases barriers to social mobility and gives rise to a divided society, with higher levels of tension, uncertainty and conflict.
Read more: How to ensure global debates about inequality are informed by views from developing countries
Undermines climate change efforts
Finally, with climate change, humans are now facing a challenge that threatens their very existence. The wealthy countries, and the elite in developing countries, are largely the cause of the problem , but the costs of climate change are likely to be borne disproportionately by low-income countries and communities. This inequality, which is of course linked to the historical trends of wealth accumulation, is likely to undermine efforts to deal with climate change, by creating resistance to change.
More equality, both within countries and across the world, is imperative if we are successfully to address the existential challenges of climate change.
Prof Sanjay G. Reddy will deliver the Southern Centre for Inequality Studies’ 2024 Inequality Lecture , The Political Economy of Global Inequality: A Drama in Three Parts, on 15 August. In partnership with the Southern Centre for Inequality Studies , The Conversation Africa has published several articles on inequality.
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IMAGES
COMMENTS
Crafting a compelling thesis statement on climate change is crucial for directing your research and presenting a clear, focused, and arguable position. A good thesis statement should be specific, take a clear stance, and be researchable and …
Humans impact the physical environment in many ways: 1. Human activity causes Environmental degradation. 2. Ecosystem disruption which is led by overpopulation 3. Habitat loss due to deforestation ...
Good Examples. Focused Approach: "This thesis will analyze the impact of climate change on the intensity and frequency of hurricanes, using data from the last three decades." Lack of Focus: "Climate change affects weather patterns." The good statement is specific, indicating a focus on hurricanes and providing a time frame. In contrast, the bad statement is too vague, covering a broad ...
The causes of global warming are complex, including natural and man-made emissions of carbon dioxide and methane. Use your thesis to highlight the difference between natural sources and man-made sources. For example, according to the Environmental Protection Agency, carbon dioxide concentrations in the atmosphere have risen from 280 parts per ...
Thesis Statement For Climate Change Argumentative Essay. The thesis statement should be a clear and concise description of your opinion on the topic. It should be established early in the essay and reiterated throughout. ... A good thesis statement for a climate change essay should state the main point or argument you will make in your essay.
Thesis statement: Environmentalists say that there are more and more frequent sharp changes in weather, storm winds, hurricanes, tornadoes, and abnormally high and abnormally low temperatures. According to experts, the cause of these phenomena is the global climate change.
Various studies and surveys show that social consensus on climate change is stronger in Europe than in the United States, where only 12% of citizens are aware of the scientific community's near ...
Now that you have chosen a topic, develop a thesis statement for your climate change topic. An example could be "The hospitality sector's global initiative has contributed to the climate crisis." A thesis statement is a statement which holds or supports the argument in the topic of your paper. Additionally, it lays out the purpose of the ...
Craft the outline and don't go off-topic. Search for keywords. Make a plan. Avoid the most common mistakes from the start. Write an introduction thinking about what you will write later. Develop your ideas according to the outline. Make a conclusion which is consistent with what you've written in the main paragraphs.
With a growing production of climate knowledge worldwide, the number of peer-review papers with the keyword "climate change" published every year has doubled within the time span of the IPCC AR6, from around 30,000 per year in 2015 to more than 60,000 per year in 2022 , with around 2/3 arising from ocean and atmosphere sciences. While peer ...
Climate Explained, a part of Yale Climate Connections, is an essay collection that addresses an array of climate change questions and topics, including why it's cold outside if global warming is real, how we know that humans are responsible for global warming, and the relationship between climate change and national security.
Summary. Subject (s): Earth Science. Topic: Climate Change and Sustainability. Grade/Level: 9-12 (can be adapted to grades 6-8) Objectives: Students will be able to write a scientific argument using evidence and reasoning to support claims. Students will also be able to reflect on the weaknesses in their own arguments in order to improve their ...
A major challenge in understanding and implementing nature-based approaches to climate change adaptation and mitigation is that of scalability. Climate change is a global problem, requiring multi-jurisdictional and multinational governance, yet many of the examples of NbS concern proof of concept studies over relatively small spatial scales.
This thesis has outlined new and truly interesting results and highlighted new avenues for research. I look forward to pursuing some of ... Climate Change and Society .....42 1.5. ADDRESSING CLIMATE CHANGE.....44 1.5.1. Addressing climate change at the community level: The role of sustainable ...
From a scientific standpoint, the causes of current ongoing climate change are well established. But in the context of rapid change, and real-world consequences, there is still room — and need ...
The Kyoto Protocol was established in 1997, with the goal of reducing greenhouse gas emissions in industrialized nations to 5% below 1990 levels by 2012. At the 4th Convention of the Parties (COP 4) in 1998, parties adopted the ÒBuenos Aires Plan of ActionÓ to further outline the implementation issues of Kyoto.
In this thesis, I measure damages and adaptation to recent climate change in three essays. First, in joint work with Sylvia Klosin, I develop a novel debiased machine learning approach to measure continuous treatment effects in panel settings. We demonstrate benefits of this estimator over standard machine learning or classical statistics ...
Across UBC, faculty and students contribute to research on climate change. See below for recent theses on a few select topics, and search cIRcle, UBC's open access repository, for publications, theses/dissertation, and presentations to find more.. RSS feed searching the UBC Theses and Dissertations Collection for: "Global warming" OR "Climate change" OR "Greenhouse gas" OR "Renewable energy":
This thesis aims to more fully describe and understand early twentieth century scientific research on the human causes of climate change in the context of similar work on the origin of ice ages. I propose to answer the following questions: 1. How were the theories on the origin of the ices ages at the beginning of the twentieth
e are many different ways to be a climate changeactivist. In this thesis, clima. e change activism is defined as taking concrete actions toadvocate for a change. carbon, but also may be aimed at larger environmental andsocial issue. For my research, I interviewed 18 college students who are involved in climate.
Pages: 4 Words: 1247. Climate change, divorced from the political rhetoric, is a concrete phenomenon affecting multiple systems. The economic and social ramifications of climate change are ancillary to its measurable physical effects. However, the measurable physical effects vary depending on geographic factors.
A guide to University of Cincinnati resources on the topics of climate change and global warming. Databases and ETD help.
Thesis Topics. The dissertation projects of the DK (in the first phase from 2014 to 2018) contribute to finding answers to three questions: How do we understand and deal with climate change uncertainties in the natural and social sciences as well as from the perspective of normative theories? What are critical thresholds of environmental ...
Climate change has emerged as one of the most significant challenges in modern agriculture, with potential implications for global food security. The impact of changing climatic conditions on crop yield, particularly for staple crops like wheat, has raised concerns about future food production. By integrating historical climate data, GCM (CMIP3) projections, and wheat-yield records, our ...
Climate change has far-reaching impacts worldwide, and an increasing amount of literature cites the disproportionate burden climate change has on disenfranchised populations and those who have the least means to adapt. Climate change also has ramifications at multiple scales, local through provincial, for Newfoundland and Labrador (NL), Canada.
C ONCLUSION. This document explains that there are well-understood physical mechanisms by which changes in the amounts of greenhouse gases cause climate changes. It discusses the evidence that the concentrations of these gases in the atmosphere have increased and are still increasing rapidly, that climate change is occurring, and that most of ...
Unsettled Ecologies: Alienated Species, Indigenous Restoration, and U.S. Empire in a Time of Climate Chaos. Fink, Lisa (University of Oregon, 2024-01-10) This dissertation traces environmental thinking about invasive species from Western-colonial, diasporic settlers of color, and Indigenous perspectives within U.S. settler colonialism.
Former President Donald Trump delivered his usual bombardment of false claims - at least 20 in all - during a Monday conversation with billionaire supporter Elon Musk, which was aired on Musk ...
The wealthy countries, and the elite in developing countries, are largely the cause of the problem, but the costs of climate change are likely to be borne disproportionately by low-income ...
According to a prediction map from a non-profit climate organization, Climate Central, Trump's Florida residence Mar-a-Lago is one of the thousands of properties endangered by flooding within the ...