The Topic Editors would like to warmly welcome members of The International Society for Energy Transition Studies (ISETS) and other non-member researchers to contribute to this dedicated Research Topic collaborating on the shared objectives to facilitate the engagement and the advancement of research centered around our energy systems. Environmental science has attracted the attention of more and more researchers around the globe, yet most of the analyses are based on traditional analytical techniques. It is noteworthy that although big data, deep learning, and other machine learning techniques have been applied in many different disciplines, including engineering, computer science, and medical science, these state-of-the-art analytical techniques have not been applied widely in the field of environmental science, nor in the areas of environmental economics and management. Given the powerful capability of these techniques and the increasing availability of big data, the application of them not only can supplement existing research by providing a new perspective on environmental economics and management, but also provide accurate forecasts and pragmatic policy suggestions.
The goal of this Research Topic is to re-examine important issues in environmental economics and management by employing cutting edge research methods which are based on big data, deep learning, and other machine learning techniques as well as other advanced analytical methods. Given that many important issues in environmental economics and management are exceptionally complex in nature, and the underlying relationships with the determinants are nonlinear, the application of these frontier research methods may prove particularly valuable because of their capability in modeling various complex and nonlinear relationships. Research studies based on any significant issues in environmental economics and management are welcome, given that the analyses are based on these state-of-the-art analytical techniques.
Studies related to any important issues in environmental economics and management are welcome. Authors are expected to address these significant issues from an empirical and quantitative point of view by revisiting these issues with the application of big data, deep learning, and other machine learning techniques as well as other frontier techniques. It is desirable to compare the findings derived from existing research studies which are based on traditional analytical methods with the proposed frontier research methods. Authors are encouraged to delve into burning issues or heated debates so as to provide insights for policy formulation in environmental economics and management. Some suggested themes are:
• Environmental protection and economic growth
• Inclusive growth with environmental protection
• Inequality and environmental degradation
• Investment and environmental degradation
• Poverty eradication with environmental protection
• Supply chain relocation and environmental impacts
• Sustainable economic growth with environmental protection
• Energy markets and environment
• Energy transition and environment
• Any related environmental, energy economics, and policy issues
The Topic Editors would like to warmly welcome members of The International Society for Energy Transition Studies (ISETS) and other non-member researchers to contribute to this dedicated Research Topic collaborating on the shared objectives to facilitate the engagement and the advancement of research centered around our energy systems. Environmental science has attracted the attention of more and more researchers around the globe, yet most of the analyses are based on traditional analytical techniques. It is noteworthy that although big data, deep learning, and other machine learning techniques have been applied in many different disciplines, including engineering, computer science, and medical science, these state-of-the-art analytical techniques have not been applied widely in the field of environmental science, nor in the areas of environmental economics and management. Given the powerful capability of these techniques and the increasing availability of big data, the application of them not only can supplement existing research by providing a new perspective on environmental economics and management, but also provide accurate forecasts and pragmatic policy suggestions.
The goal of this Research Topic is to re-examine important issues in environmental economics and management by employing cutting edge research methods which are based on big data, deep learning, and other machine learning techniques as well as other advanced analytical methods. Given that many important issues in environmental economics and management are exceptionally complex in nature, and the underlying relationships with the determinants are nonlinear, the application of these frontier research methods may prove particularly valuable because of their capability in modeling various complex and nonlinear relationships. Research studies based on any significant issues in environmental economics and management are welcome, given that the analyses are based on these state-of-the-art analytical techniques.
Studies related to any important issues in environmental economics and management are welcome. Authors are expected to address these significant issues from an empirical and quantitative point of view by revisiting these issues with the application of big data, deep learning, and other machine learning techniques as well as other frontier techniques. It is desirable to compare the findings derived from existing research studies which are based on traditional analytical methods with the proposed frontier research methods. Authors are encouraged to delve into burning issues or heated debates so as to provide insights for policy formulation in environmental economics and management. Some suggested themes are:
• Environmental protection and economic growth
• Inclusive growth with environmental protection
• Inequality and environmental degradation
• Investment and environmental degradation
• Poverty eradication with environmental protection
• Supply chain relocation and environmental impacts
• Sustainable economic growth with environmental protection
• Energy markets and environment
• Energy transition and environment
• Any related environmental, energy economics, and policy issues