Are carbon dioxide emission reductions compatible with sustainable well-being?

[1]  Wilfred Beckerman,et al.  Economic growth and the environment: Whose growth? whose environment? , 1992 .

[2]  T. Panayotou.  Empirical tests and policy analysis of environmental degradation at different stages of economic development , 1993 .

[3]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[4]  M. Qizilbash.  The Concept of Well-Being , 1998, Economics and Philosophy.

[5]  P. Dasgupta,et al.  Net national product, wealth, and social well-being , 2000, Environment and Development Economics.

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[8]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[9]  Max Kuhn,et al.  The caret Package , 2007 .

[10]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[11]  Aylin Çiğdem Köne,et al.  Forecasting of CO2 emissions from fuel combustion using trend analysis , 2010 .

[12]  H. Pao,et al.  Modeling and forecasting the CO 2 emissions, energy consumption, and economic growth in Brazil , 2011 .

[13]  Dongxiao Niu,et al.  Modeling CO 2 emissions from fossil fuel combustion using the logistic equation , 2011 .

[14]  K. Arrow,et al.  Sustainability and the measurement of wealth , 2010, Environment and Development Economics.

[15]  Brett Lantz,et al.  Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications , 2013 .

[16]  Jan Christoph Steckel,et al.  Development without energy? Assessing future scenarios of energy consumption in developing countries , 2013 .

[17]  Jean-Francois Lamarque,et al.  Co-benefits of Global Greenhouse Gas Mitigation for Future Air Quality and Human Health , 2013, Nature climate change.

[18]  E. Zervas,et al.  The Environmental Kuznets Curve (EKC) theory—Part A: Concept, causes and the CO2 emissions case , 2013 .

[19]  R. Costanza,et al.  Development: Time to leave GDP behind , 2014, Nature.

[20]  Kirk Hamilton,et al.  Wealth and sustainability , 2014 .

[21]  Claudia Senik,et al.  Wealth and happiness , 2014 .

[22]  Roberto Turconi,et al.  Environmental impacts of future low-carbon electricity systems: Detailed life cycle assessment of a Danish case study , 2014 .

[23]  Erdong Zhao,et al.  Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development , 2014 .

[24]  Alfredo Marvão Pereira,et al.  An alternative reference scenario for global CO2 emissions from fuel consumption: An ARFIMA approach , 2015 .

[25]  Analyzing and forecasting the global CO2 concentration — a collaborative fuzzy-neural agent network approach , 2015 .

[26]  Sifeng Liu,et al.  Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model , 2015 .

[27]  Rigoberto Pérez-Suárez,et al.  Growing green? Forecasting CO2 emissions with Environmental Kuznets Curves and Logistic Growth Models , 2015 .

[28]  Ilhan Ozturk,et al.  Testing Environmental Kuznets Curve hypothesis in Asian countries , 2015 .

[29]  Guangfei Yang,et al.  Modeling the nexus between carbon dioxide emissions and economic growth , 2015 .

[30]  P. Dasgupta,et al.  How to measure sustainable progress. , 2015, Science.

[31]  I. Ozturk,et al.  What drives carbon dioxide emissions in the long-run? Evidence from selected South Asian Countries , 2016 .

[32]  K. Mumford.  Prosperity, Sustainability and the Measurement of Wealth , 2016 .

[33]  D. Andrei.  Basics of Sustainable Development , 2016 .

[34]  Miloš Milovančević,et al.  Prediction of GDP growth rate based on carbon dioxide (CO2) emissions , 2016 .

[35]  M. Western,et al.  Subjective Wellbeing, Objective Wellbeing and Inequality in Australia , 2016, PloS one.

[36]  Mohan Liu,et al.  Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China , 2016 .

[37]  Gitta H. Lubke,et al.  Finding structure in data using multivariate tree boosting , 2015, Psychological methods.

[38]  Yogi Sugiawan,et al.  The environmental Kuznets curve in Indonesia: Exploring the potential of renewable energy , 2016 .

[39]  Wei Sun,et al.  Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization , 2017 .

[40]  Anis Omri,et al.  Literature survey on the relationships between energy, environment and economic growth , 2017 .

[41]  Zakaria Zoundi.  CO2 emissions, renewable energy and the Environmental Kuznets Curve, a panel cointegration approach , 2017 .

[42]  Noelle E. Selin,et al.  Using Inclusive Wealth for Policy Evaluation: Application to Electricity Infrastructure Planning in Oil-Exporting Countries , 2016 .

[43]  Tetsuya Tamaki,et al.  Inclusive wealth of regions: the case of Japan , 2017, Sustainability Science.

[44]  J. Francois,et al.  Carbon Dioxide Emissions and Economic Growth: An Assessment Based on Production and Consumption Emission Inventories , 2017 .

[45]  George Filis,et al.  Energy consumption, CO2 emissions and economic growth: an ethical dilemma , 2017 .

[46]  Khalid Zaman,et al.  Energy consumption, carbon dioxide emissions and economic development: Evaluating alternative and plausible environmental hypothesis for sustainable growth , 2017 .

[47]  Xuemei Li,et al.  Forecasting Chinese CO 2 emissions from fuel combustion using a novel grey multivariable model , 2017 .

[48]  Zheng-Xin Wang,et al.  Forecasting Chinese carbon emissions from fossil energy consumption using non-linear grey multivariable models , 2017 .

[49]  Unu-Ihdp.  Inclusive wealth report 2012: Measuring progress toward sustainability , 2017 .

[50]  Madhumita Bhattacharya,et al.  The dynamic impact of renewable energy and institutions on economic output and CO2 emissions across regions , 2017 .

[51]  Tobias Böhmelt,et al.  Employing the shared socioeconomic pathways to predict CO 2 emissions , 2017 .

[52]  B. Elliston,et al.  The feasibility of 100% renewable electricity systems: A response to critics , 2018, Renewable and Sustainable Energy Reviews.

[53]  Jihua Zhang,et al.  Measurement of the ocean wealth of nations in China: An inclusive wealth approach , 2018 .

[54]  R. Kurniawan,et al.  Economic Growth and Sustainable Development in Indonesia: An Assessment , 2018, Bulletin of Indonesian Economic Studies.

[55]  G. Faluvegi,et al.  Quantified, Localized Health Benefits of Accelerated Carbon Dioxide Emissions Reductions , 2018, Nature Climate Change.

[56]  S. Managi,et al.  Inclusive Wealth Report 2018 , 2018 .

[57]  Xiaoling Zhang,et al.  A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression , 2018 .

[58]  S. Myers,et al.  Impact of anthropogenic CO2 emissions on global human nutrition , 2018, Nature Climate Change.

[59]  Tsangyao Chang,et al.  Nexus between clean energy consumption, economic growth and CO2 emissions , 2018 .

[60]  K. Takeuchi,et al.  Assessing local-scale inclusive wealth: a case study of Sado Island, Japan , 2018, Sustainability Science.

[61]  Taehoon Hong,et al.  An optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms , 2018, Applied Energy.

[62]  Yogi Sugiawan,et al.  Valuing natural capital and ecosystem services: a literature review , 2018, Sustainability Science.

[63]  R. Kurniawan,et al.  Linking Wealth and Productivity of Natural Capital for 140 Countries Between 1990 and 2014 , 2019 .

[64]  Yogi Sugiawan,et al.  New evidence of energy-growth nexus from inclusive wealth , 2019, Renewable and Sustainable Energy Reviews.