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The main goal of this paper is to argue for the necessity and significance of studying the distributional employment impacts of renewable and new energy development (RNE). Based on the comprehensive review of the methodology and conclusions of existing literatures, this paper builds up an extended input–output model to study RNE’s distributional employment impacts on gender and personnel structure. The case study of China׳s power sector in this paper affirmed earlier doubts that RNE development will indeed aggravate the gender inequality problem and add to the level of mismatch between the structure of labor demand and supply, causing structural unemployment problems. The quantitative analysis in this paper outlined here implies that from 2011 to 2020 the development of RNE will bring about 7 million employment gains, but only 81.8% of which can be realized due to the mismatch problem. The study of China may alert other countries to be less-optimistic about RNE’s employment impacts and reaffirm the need to carry out the distributional employment impacts analysis. This paper concludes with policy suggestions such as providing suitable training and equal promotion opportunities for women, offering courses and vocational trainings to RNE-related majors, in order to reduce the structural unemployment problem and further speed up the development of RNE. [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]

Abstract: There are many kinds of methods to estimate the energy requirement or fuel consumption in the optimal design and minimize the life cost of a building. The bin method is one of the most popular ways to estimate the energy performance of a building, which is based on the hourly-based outdoor dry-bulb temperatures of all days in a whole year. There is lack of information about the bin weather data in China. In this paper, the ambient temperature bin data for 26 cities in China are generated. Based on the method used in this paper, the bin data can be calculated with the long-term daily weather record or TMY weather data, the deviation between them is acceptable. The bin data given in this paper may have the positive impact on building energy conservation in China. [Copyright &y& Elsevier]