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The environment constraints have become more restrictive over time, because the transformation of energy consumption structure (ECS) is the key path to achieve the commitment of net-zero emission. However, the adjustment of ECS involves the influence of many factors, including economic factors, energy factors and social factors. Not to mention how ECS -driving factors under tougher environmental regulation may behave throughout long time period remains explored. According to the background, the periodic driving factors of industrial ECS in China based on the periodic effect of environmental regulation on ECS are discussed. In this paper, industries of China are divided into four sectors from 1998 to 2017, and a multi-layer LMDI decomposition model is constructed to decompose ECS of the four sectors in stages. Since the industrial and commercial sector provides the largest contribution rate, the LMDI model based on PDA production theory method is applied to decompose industrial and commercial ECS , aiming to deeply explore the main driving factors of ECS in subindustries, and formulate scientific energy and environmental policies. The results show that from the perspective of the whole industry, the per capita pollution control investment has the most significant negative contribution, and the economic activity effect provides a great positive contribution to the ECS. In terms of subindustries, the main negative drivers are potential energy intensity and GDP technology change. The comprehensive decomposition framework. [Display omitted] • The drivers of ECS in four sectors are identified. • An integrated decomposition measurement combined LMDI with PDA is constructed. • EP is major inhibiting factor of ECS optimizing in four sectors. • PE and GT are major inhibiting factors of ECS optimizing in subsectors. • Some policy recommendations to optimize energy consumption structure are purposed. [ABSTRACT FROM AUTHOR]

Based on the international community's analysis of the present CO2 emissions situation, a Log Mean Divisia Index (LMDI) decomposition model is proposed in this paper, aiming to reflect the decomposition of carbon productivity. The model is designed by analyzing the factors that affect carbon productivity. China's contribution to carbon productivity is analyzed from the dimensions of influencing factors, regional structure and industrial structure. It comes to the conclusions that: (a) economic output, the provincial carbon productivity and energy structure are the most influential factors, which are consistent with China's current actual policy; (b) the distribution patterns of economic output, carbon productivity and energy structure in different regions have nothing to do with the Chinese traditional sense of the regional economic development patterns; (c) considering the regional protectionism, regional actual situation need to be considered at the same time; (d) in the study of the industrial structure, the contribution value of industry is the most prominent factor for China's carbon productivity, while the industrial restructuring has not been done well enough. [ABSTRACT FROM AUTHOR]

The Greater Bay Area (GBA) of China is experiencing a high level of exposure to outdoor PM2.5 pollution. The variations in the exposure level are determined by spatiotemporal variations in the PM2.5 concentration and population. To better guide public policies that aim to reduce the population exposure level, it is essential to explicitly decompose and assess the impacts of different factors. This study took advantage of high-resolution satellite observations to characterize the long-term variations in population exposure to outdoor PM2.5 for cities in the GBA region during the three most-recent Five-Year Plan (FYP) periods (2001–2015). A new decomposition method was then used to assess the impact of PM2.5 variations and demographic changes on the exposure variation. Within the decomposition framework, an index of pollution-population-coincidence–induced PM2.5 exposure (PPCE) was introduced to characterize the interaction of PM2.5 and the population distribution. The results showed that the 15-year average PPCE levels in all cities were positive (e.g., 6 µg/m3 in Guangzhou), suggesting that unfavorable city planning had led to people dwelling in polluted areas. An analyses of the spatial differences in PM2.5 changes showed that urban areas experienced a greater decrease in PM2.5 concentration than did rural areas in most cities during the 11th (2006–2010) and 12th (2011–2015) FYP periods. These spatial differences in PM2.5 changes reduced the PPCE levels of these cities and thus reduced the exposure levels (by as much as -0.58 µg/m3/year). The population migration resulting from rapid urbanization, however, increased the PPCE and exposure levels (by as much as 0.18 µg/m3/year) in most cities during the three FYP periods considered. Dongguan was a special case in that the demographic change reduced the exposure level because of its rapid development of residential areas in cleaner regions adjacent to Shenzhen. The exposure levels in all cities remained high because of the high mean PM2.5 concentrations and their positive PPCE. To better protect public health, control efforts should target densely populated areas and city planning should locate more people in cleaner areas. [ABSTRACT FROM AUTHOR]