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The five major power generation corporations dominate the power industry in China, and play vital roles in China's carbon trading scheme. Under this circumstance, this paper studies the allocation of carbon emission quotas to China's five major power generation corporations based on the fairness and efficiency principles, which proves the primary prerequisite for setting up the national carbon market. Specifically, a bi-level programming model is developed to optimally allocate carbon emission quotas to the corporations, and then a zero sum gains data envelopment analysis (ZSG-DEA) model is adopted to evaluate the efficiency of the allocation so as to adjust towards the optimal solution. The results indicate that power generation has significantly positive influence on the allocation of carbon emission quotas. Moreover, the difference between the sum of optimal carbon emissions and the sum of carbon quotas allocated is 1.431 billion tons, which implies that the emission reduction potential of the five major power generation corporations is tremendous. Finally, the bi-level model in this paper can well capture the context of China's power industry and provide effective results for the allocation of carbon quotas. [ABSTRACT FROM AUTHOR]

Abstract Theses on China's low-carbon economy focus on the driving forces of carbon emissions, energy–carbon efficiency, and the carbon–economy nexus. Meanwhile, comprehensive frameworks that assess China's green economy and also analyze its driving forces are lacking. This paper constructs a green productivity index based on the Global Malmquist–Luenberger productivity index to evaluate the development of the low-carbon economy in China. A panel vector auto-regression model is adopted to further analyze the endogenous interactions and dynamic relationships among aspects of the green economy and its influencing factors. Empirical results show that China has seen a balanced development of green economic growth in 2000–2016, achieved in a series of clear stages. To maintain the sustainable growth of the low-carbon economy in the future, China must rely more on technological progress. This research also makes some important discoveries that have not been found in previous studies: the impact of China's industrial structure and energy consumption structure on the green economy are short term, having a duration of impact of approximately four years. Finally, we provide an assessment of provincial green economy development and describe the corresponding policy implications. Graphical abstract Image Highlights • A comprehensive analysis of China's green economy. • A slack-based Global Malmquist–Luenberger index is adopted to calculate the Green Productivity Index in China. • The stage characteristics of green economic development are identified. • A panel vector autoregressive model is used to analyze the interactions among aspects of the environmental economic system. • The findings that the impact duration of energy structure, industry structure, and urbanization on the green economy is about four years. [ABSTRACT FROM AUTHOR]

The 2015 Paris climate change negotiations represents the most ambitious worldwide effort to date to combat climate change. As the largest energy consumer and carbon emitter, China pledges 60–65% cut in carbon intensity below 2005 levels by 2030. To accelerate China's climate efforts, China has also committed to cap its primary energy use at 4.8 billion tons of coal equivalent in 2020. This paper proposes an equitable and efficient scheme to disaggregate this national goal among provinces. A three-step disaggregation procedure is proposed which takes into account the initial allocation, national redistribution and inter-provincial adjustment. First, this study allocates the incremental energy quota, taking 2010 as the base year. Second, part of the energy quota is set aside by the central government for redistribution purposes, while an incremental energy quota index comprising equity and efficiency criteria is used for initial allocation among provinces. Finally, historical efforts and inter-provincial energy flows are considered for inter-provincial adjustment. This disaggregation scheme is flexible and can be easily extended to allocation and management of other resource and environmental targets. The empirical findings can assist decision-makers in identifying which provinces are likely to over-consume the energy quota relative to the proposed allocation and which provinces are likely to perform better in energy conservation. Moreover, the results show that the energy cap set for 2020 is unachievable without further action. Market-based instruments, such as an energy allowance trading market, are strongly recommended to achieve the energy use target in an equitable and cost-effective manner. [ABSTRACT FROM AUTHOR]