• Energy Research

There are substantial differences in carbon footprints across households. This study applied an environmentally extended multiregional input–output approach to estimate household carbon footprints for 12 different income groups of China’s 30 regions. Subsequently, carbon footprint Gini coefficients were calculated to measure carbon inequality for households across provinces. We found that the top 5% of income earners were responsible for 17% of the national household carbon footprint in 2012, while the bottom half of income earners caused only 25%. Carbon inequality declined with economic growth in China across space and time in two ways: first, carbon footprints showed greater convergence in the wealthier coastal regions than in the poorer inland regions; second, China’s national carbon footprint Gini coefficients declined from 0.44 in 2007 to 0.37 in 2012. We argue that economic growth not only increases income levels but also contributes to an overall reduction in carbon inequality in China.

AbstractEnergy consumption is one of main reasons for global warming and highly correlated with economic development. As the largest energy consumer worldwide, China has entered a new economic development model—the “new normal.” This study aims to explore the pattern shift in China's energy consumption growth in this new development phase. We use structural decomposition analysis and environmentally extended input‐output analysis to decompose China's energy consumption changes during 2005–2012 into five factors: population, efficiency, production structure, consumption patterns, and consumption volume. During the period of the global financial crisis, the energy consumption generated by China's exports dropped, while the energy consumption generated by capital formation grew rapidly. Over three quarters of China's energy consumption growth was caused by capital formation during 2007–2010. This growth is mainly because of China's economic stimulus measures in response to the global recession, with a focus on infrastructure construction. In the new normal, the strongest factors offsetting China's energy consumption have been shifting from efficiency gains to structural changes. Efficiency gains were the strongest factor offsetting China's energy consumption in traditional development model and offset 42% of energy consumption between 2005 and 2010 by keeping other driving forces constant. Since 2010, however, their effects offsetting energy have become weak. The production structure and consumption patterns both drove China's energy consumption growth in the traditional development model and drove energy consumption growth by 31% and 12% between 2005 and 2010, respectively. Since 2010, however, both factors have started to offset China's energy consumption.

Carbon emission inventories are the foundations of climate change mitigation and adaptation in cities. In this study, we estimated production-based CO 2 emissions from fossil fuel combustion and industrial processes in eleven cities in Hebei Province of China in 2012 and used input-output theory to measure their consumption-based CO 2 emissions. By comprehensively comparing production- and consumption-based emissions, we found that six developed cities were consumers with import-depended trade patterns, while the five other cities were producers, mostly medium in size, with the potential to transform into consumer cities with socioeconomic development. Emissions embodied in imports accounted for more than half of the consumption-based emissions in most cities, which shows the significance of interregional cooperation in tackling climate change. International cooperation is also important at the city level, as international imports also impact consumption-based emissions. From the perspective of final use, emissions caused by fixed capital formation predominated in most cities and were determined by their economic development models.

Abstract Delay in publication of energy statistics prevents a timely assessment of progress towards meeting targets for energy saving and emission reduction in China. This makes it difficult to meet the requirements to rapidly monitor and evaluate energy consumption for each province. In this study, an alternative approach is provided to estimate the energy consumption by using satellite remote sensing data. We develop spatio-temporal geographically weighted regression models to simulate energy consumption of provinces in China based on the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) global stable night-time light data. The models simulate China’s energy consumption accurately with the goodness of fit higher than 99%. Generally, the national average annual energy consumption is 2.8 billion tonnes of coal equivalent in China between 2000 and 2013, which is close to the actual value with errors smaller than 0.1%. From both temporal and spatial dimensions, the relative errors are smaller than 5.5% at the provincial level. Therefore, the use of satellite night-time light data provides a useful reference in monitoring and assessing provincial energy consumption in China.

China announced at the Paris Climate Change Conference in 2015 that the country would reach peak carbon emissions around 2030. Since then, widespread attention has been devoted to determining when and how this goal will be achieved. This study aims to explore the role of China’s changing regional development patterns in the achievement of this goal. This study uses the logarithmic mean Divisia index (LMDI) to estimate seven socioeconomic drivers of the changes in CO2 emissions in China since 2000. The results show that China’s carbon emissions have plateaued since 2012 mainly because of energy efficiency gains and structural upgrading (i.e., industrial structure, energy mix and regional structure). Regional structure, measured by provincial economic growth shares, has drastically reduced CO2 emissions since 2012. The effects of these drivers on emissions changes varied across regions due to their different regional development patterns. Industrial structure and energy mix resulted in emissions growth in some regions, but these two drivers led to emissions reduction at the national level. For example, industrial structure reduced China’s CO2 emissions by 1.0% from 2013-2016; however, it increased CO2 emissions in the Northeast and Northwest regions by 1.7% and 0.9%, respectively. By studying China’s plateauing CO2 emissions in the new normal stage at the regional level, it is recommended that regions cooperate to improve development patterns.

Since 2013, China's economy has undergone a series of major structural changes under the new normal. This study aimed to research China's plateauing regional-level energy consumption at this stage by analysing socioeconomic factors driving energy consumption changes from 2002 to 2019 through decomposition analysis and regional value chains. The results indicate that the annual growth rate of China's energy consumption dropped from 10% between 2002 and 2013 to 2% between 2013 and 2019, mainly attributable to energy efficiency enhancement offsetting the −27% increase from 2013 to 2019 and structural changes. At the regional level, the three structural drivers were closely related, including the regional structure, industrial structure and energy structure. Under the new normal, the −2.58% contribution of the regional structure to energy consumption growth was mainly made by regions with a high energy efficiency; one way to improve the energy efficiency was to upgrade the regional industrial structure, leading to the slowdown by 0.26%; and industrial transition could be accompanied by adjustment of the energy structure towards relatively clean energy, thereby offsetting growth by −0.13%. The energy consumption required to create value-added outflows along regional value chains varied greatly across regions, sectors and years.

Abstract A decomposition-clustering-ensemble (DCE) learning approach is proposed for solar radiation forecasting in this paper. In the proposed DCE learning approach, (1) ensemble empirical mode decomposition (EEMD) is used to decompose the original solar radiation data into several intrinsic mode functions (IMFs) and a residual component; (2) least square support vector regression (LSSVR) is performed to forecast IMFs and residual component respectively with parameters optimized by gravitational search algorithm (GSA); (3) Kmeans method is adopted to cluster all component forecasting results; (4) another GSA-LSSVR method is applied to ensemble the component forecasts of each cluster and the final forecasting results are obtained by means of corresponding cluster’s ensemble weights. To verify the performance of the proposed DCE learning approach, solar radiation data in Beijing is introduced for empirical analysis. The results of out-of-sample forecasting power show that the DCE learning approach produces smaller NRMSE, MAPE and better directional forecasts than all other benchmark models, reaching up to accuracy rate of 2.96%, 2.83% and 88.24% respectively in the one-day-ahead forecasting. This indicates that the proposed DCE learning approach is a relatively promising framework for forecasting solar radiation by means of level accuracy, directional accuracy and robustness.

The importance of good performance in the logistics industry contributing towards a low-carbon economy is widely recognised. However, there are few studies on carbon emissions performance for the logistics industry, especially at the city level. Therefore, this study attempts to analyse the carbon emissions performance of the logistics industry at the city level by examining sixteen cities within Yunnan Province. In particular, the Data envelopment analysis (DEA) model and Malmquist index from both the static and dynamic perspective were explored. To further capture the driving factors of the carbon emissions performance in logistics, the Tobit model was applied to perform regression analysis. The results indicate that (1) with regards to static performance, two northwest cities, Nujiang and Diqing, have reached the technological frontier; (2) regarding dynamic performance, although six cities have improved, the average dynamic logistics carbon emissions performance of all the cities decreases by approximately 5.9% from 2011 to 2015; (3) in terms of driving factors, the economic development factor is found to be positively and significantly related to static carbon emissions performance, whereas others indicate negative and significant relationships.