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Abstract In this study, we investigate the driving forces behind the changes in residential energy consumption (REC) in China’s urban and rural areas over the 2001–2012 period. Based on the logarithmic mean Divisia index method, the REC changes are decomposed into seven driving forces, which are climate change, energy price, energy expenditure mix, energy cost share (in total expenditure), expenditure share (in income), per capita income and population effects. According to the results, climate effect due to increasing days with abnormal temperature, energy cost share effect characterized by more expenditure to be paid for energy use, income effect describing constant income growth in the residential sector definitely increase REC in both urban and rural areas. In contrast, energy prices and energy expenditure mix effects negatively contribute to the REC increase, respectively because of the increase in energy prices and the transition from the low-priced energy to high-priced energy. Expenditure share and population effects play opposite roles in urban and rural areas, and the reasons and implications are analysed in depth.

The emerging concept of bioeconomy offers several opportunities to address societal challenges. The bioeconomy is mainly driven by advances in microbiology, which can be applied to various processes that use biological resources by shifting consumer preferences and by yielding new insights into resource constraints related to such issues as climate and land. Although expectations are high, less is known about the economic importance of the bioeconomy. This article reviews the methodological challenges of measuring the bioeconomy, the approaches used, and the outcomes reported. The results show that measuring the bioeconomy is still in its infancy and faces a number of methodological challenges. Bioeconomy cuts across sectors and therefore cannot be treated as a traditional sector in economics. Economics must catch up with bioeconomy realities. For a comprehensive economic assessment, information about bioeconomy resources, compounds, and product flows is required. We outline innovations in data storage and analytical methods that would realize bioeconomy opportunities and help guide policy.

This paper provides an empirical analysis of energy-productivity convergence across 56 developed and developing countries, in 10 manufacturing sectors, for the period 1971-1995. We find that, except for the non-ferrous metals sector, cross-country differences in absolute energy-productivity levels tend to decline, particularly in the less energy-intensive industries. Testing for the catch-up hypothesis using panel data confirms that in all manufacturing sectors energy-productivity growth is, in general, relatively high in countries that initially lag behind in terms of energy-productivity levels. At the same time, cross-country differences in energy-productivity performance seem to be persistent; convergence is found to be local rather than global, with countries converging to different steady states and several failing to catch up. Finally, we find that country-specific factors, such as energy price and investment ratio, do explain the observed cross-country differences in energy-productivity performance, but only to a very limited extent.

The energy-related CO2 emissions in China have increased dramatically from 3384 to 8333 × 106 t during the last decade. To interpret these drastic changes, we undertake a structural decomposition analysis to decompose the changes in CO2 emissions from 1997 to 2010 into the following six driving forces: emission coefficient, energy intensity, Leontief, sectoral structure, demand allocation (the shares of consumption, investments, and exports in final demand), and final demand effects. The results show that declines in energy intensity had a decrease impact on CO2 emissions during the studied period. Changes in the relative importance of intermediate production in total output (the Leontief effect) contributed to decrease CO2 emissions in the 2000–2002 period and to increase emissions in the other periods. The most important driver behind the steady increase in CO2 emissions is the large increase in final demand. A further analysis at the sectoral level revealed differences and fluctuations between sectors. Energy intensity fell most strongly in the electric power sector and the coking, gas, and petroleum production sector (two energy-intensive sectors). The shift toward exports and investment increased CO2 emissions (demand allocation effect). Part of the increases in CO2 emissions thus stem from production activities for consumption activities elsewhere.

A CGE model of South Africa is used to find the potential for a double or triple dividend if the revenues raised from an energy-related environmental tax are recycled to households and industry through lowering existing taxes. Four environmental taxes and three revenue-recycling schemes are compared. The environmental taxes are (i) a tax on greenhouse gas emissions, (ii) a fuel tax, (iii) a tax on electricity use, and (iv) an energy tax. The four taxes are constructed such that they have a comparable effect on emissions. The revenue is recycled through either (i) a direct tax break on both labour and capital, (ii) an indirect tax break to all households, or (iii) a reduction in the price of food. A triple dividend is found - decreasing emissions, increasing GDP, and decreasing poverty - when any one of the environmental taxes is recycled through a reduction in food prices.

AbstractUnder the growing threats of climate change, innovation and pollution reduction have become driving forces for cleaner economic growth and the environment. This study endeavors to analyze the effect of economic complexity—understood as structural transformation toward more sophisticated and knowledge‐based production, economic progress, renewable energy consumption, and population growth over carbon emissions. Our study employs panel data for a sample of 28 OECD countries covering the period of 1990–2014. The main contribution to the energy economics literature is given by bringing together the concept of environmental degradation and economic complexity controlling via renewable energy consumption and economic and population growth. Based on the extensive empirical analysis (augmented mean group estimator, panel cointegration, and panel regression techniques), we conclude that economic complexity and renewable energy might help in mitigating the environmental degradation problems in OECD countries.