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Green total factor productivity is a vital measure of sustainable progress, holding immense implications for attaining both economic growth and environmental preservation in a mutually beneficial manner. Focusing on green total factor productivity, this paper examines its nexus with low-carbon energy technology using the Instrumental Variable Generalized Method of Moments (IV-GMM) model, based on a panel dataset in China during 2005–2018. This paper also examines the asymmetric impact of low-carbon energy technology on green total factor productivity. In addition, the moderating and mediating effects are checked. The main findings are that (1) low-carbon energy technology is a promoting factor for green total factor productivity. (2) Low-carbon energy technology exerts a more prominent marginal impact on green total factor productivity when green total factor productivity is at higher quantile levels. (3) Renewable energy generation not only drives green total factor productivity directly, but also further increases the promotion effect of low-carbon energy technology on green total factor productivity, showing a positive moderating role. (4) Energy structure transition and industrial structure transition are two internal impact mechanisms between low-carbon energy technology and green total factor productivity. These findings suggest that the government can promote green total factor productivity development by developing policies related to low-carbon energy technology and fully releasing the effects of technology on sustainable development. • This paper assesses the level of low-carbon energy technology (LCET) in China. • This paper examines the effect of LCET on green total factor productivity (GTFP). • LCET plays a significant role in enhancing GTFP. • Renewable energy generation positively moderates the nexus between LCET and GTFP. • Energy structure transition and industrial structure transition are two mechanisms. [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]

In order to assess the viability of wind energy projects in China, this paper takes a real options approach by accounting for flexibility over deployment of the technology in response to a stochastic non-renewable energy cost. The difference between the latter and the R&D-enhanced cost of renewable energy operation may be thought of as a benefit from using wind. On the other hand, the Chinese government provides feed-in tariffs to encourage further adoption of wind, which should be included as a cost. This work, thus, shows that there is substantial options value to China's wind energy program, which is bolstered if the cost of carbon dioxide emissions is internalized. Moreover, by varying the rate of feed-in tariffs, the model may be used to show plausible ranges for the feed-in tariffs, which could be a useful policy insight. Furthermore, the study hypothesizes that with the likelihood of renewable energy to replace non-renewable energy, wind power in China can potentially reduce CO 2 emissions by approximately 6.4% by 2020 comparing to the 2005 level and highlights the significant role of wind power in China's emission targets and energy security. This is a relevant paper, especially given the importance of a sustainable energy transition for China. More generally, using the real options approach from a policy perspective could provide insights about the viability of renewable energy programs and robustness of feed-in tariffs rates. [ABSTRACT FROM AUTHOR]

In this paper, an improved hybrid MADM method based on interval Decision Making Trial and Evaluation Laboratory (DEMATEL) and interval regret theory are put forward to evaluate five renewable energy alternatives for Fujian province, China. Firstly, the volatility of the evaluation information and the subjectivity of experts or stakeholders are taken into account. Second, political factors are further considered based on existing research. Third, the influence of the subjective initiative of decision makers on the decision result has been paid attention to. Furthermore, the result of the analysis shows that the extended regret theory method should set different parameters to reflect the preferences of decision makers. The ranking results of renewable energy are different with different parameter settings. For example, when α = 0.1 , δ = 0.1 , the priority of clean energy in Fujian Province is hydropower, followed by wind energy, solar energy, geothermal energy, and biomass energy. Sensitivity analysis indicates that the model possesses good serviceability. Based on considering different parameter settings, the Fujian government should invest, in order of priority, in these renewable energy sources to obtain benefits. And the projects related to these renewable energy sources should be evaluated. [ABSTRACT FROM AUTHOR]

In recent years, China has successively issued relevant policies to support the development of distributed renewable energy sources such as dispersed wind power (DWP) and distributed photovoltaic (DPV). The capacity of DPV grows fast, but the progress of DWP deployment is limited. In this paper, the improved levelized cost of electricity (LCOE) model and the internal rate of return (IRR) are employed to evaluate the economics of DWP and DPV for case regions. We find that, under current market regime, the IRR of DWP in eastern and central regions is 8.81%–10.34% for the grid side, and which of DPV is 9.08%–12.8% for the user side. When the power market is restructured, the IRR of DWP for industrial and commercial user side increases to 12.73%–14.86%, higher than that the return of DPV. Policy implications on grid-connection, land expropriation, and financial support, planning and approval procedure are proposed to promote the development of DWP. Overall, this paper covers the research gap on the development of DWP in China with a technical/institutional economics perspective and a comparison with DPV. Furthermore, our efforts make a significant contribution to DWP policy by identifying the institutional factors that hinder the development of DWP. • Explore the technical & institutional factors around DWP deployment in China. • DWP and DPV gain comparable economic returns under current planning regime. • DWP can have better economic returns than DPV with electricity market reform. • Identify institutional factors that hinder DWP and propose policy implications. [ABSTRACT FROM AUTHOR]

Biomass power is one of the most important renewable energy sources in China. In order to provide a reference for China's biomass power planning, this paper builds a power sector-planning model using the Long-range Energy Alternatives Planning System (LEAP). The results show that in the base scenario, the installed capacity of agricultural and forestry residues, municipal solid waste and biogas will increase to 22350 MW, 21150 MW, and 4900 MW, respectively by 2030. From the point of view of total volume, biomass supply is not a constraining factor for biomass power source. However, there are some social and economic factors that impede the development of the biomass power industry, some of which may not be addressed in the short term. Therefore, the development of the biomass power industry in China is a long-term process. Some policy suggestions were proposed, including reasonable planning and more subsidies for biomass supply value chain. • An optimal power-planning model focused on bio-energy in China was developed. • Agricultural and forestry power, waste power and biogas power were all analyzed. • Social and economic factors that impede biomass power were investigated. [ABSTRACT FROM AUTHOR]