Spatially differentiated effects of socioeconomic factors on China's NOx generation from energy consumption: implications for mitigation policy

The spatiotemporal effects of environmental regulation on green innovation: Evidence from Chinese cities

Environmental regulation is expected to stimulate green innovation for the promotion of urban sustainability, while the effectiveness of this stimulus has long been debated under the Porter hypothesis and the crowding out theory. Empirical studies under different contexts have not reached a consistent conclusion yet. Based on the data of 276 cities in China from 2003 to 2013, this study captures the spatiotemporal non-stationarity in the effects of environmental regulation on green innovation with the combination of Geographically and Temporally Weighted Regression (GTWR) and Dynamic Time Warping (DTW) algorithm. The results show that environmental regulation has an overall U-shape impact on green innovation, indicating that the Porter hypothesis and the crowding out theory are not in conflict, but are theoretical interpretations of different stages of local responses to environmental regulation. Specifically, the effects of environmental regulation on green innovation present to be diverse in patterns that include enhancing, stagnant, undermining, U-shape, and inverted U-shape. These contextualized relationships are shaped by local industrial incentives and innovation capacities of pursing green transformations. The spatiotemporal findings allow policymakers to better understand the multi-staged and geographically diverse impacts of environmental regulation on green innovations, and formulate targeted policies for different localities.

Contributions of cleaner production and end-of-pipe treatment to NOx emissions and intensity reductions in China, 1997-2018

The Chinese experience of economic development and environmental protection provides an important reference for developing countries. Although changes in aggregate NO_x emissions have been widely studied, there is a relative lack of studies analysing NO_x intensity changes and their related development strategies in China. This study attempts to identify the socioeconomic drivers and change patterns for both NO_x emissions and intensity considering the cleaner production and end-of-pipe treatments. Both structural decomposition analysis and structural path analysis were used to analyse the NO_x emissions/intensity changes at different levels and transmission layers in China in the last two decades (1997-2018). The results indicate that construction contributes the most to NO_x emissions/intensity, followed by transportation. The emission intensity effect is the primary driver of NO_x emissions/intensity reduction, which mainly benefits from end-of-pipe treatment and energy efficiency improvement. Especially, during 2012-2018, they decreased 11,916 Kt-NO_x and 8,103 Kt-NO_x emissions and aggregate embodied intensity by 43.2% and 29.8%, respectively. The final demand effect is the primary deterrent, which is attributed to investment and consumption effects. The critical sectors for future NO_x reduction are the construction and building materials industry, transportation and other services industry. The policy implications and recommendations for the future developments are discussed based on the study findings.

Spatiotemporal Variations in Energy Consumption and Their Influencing Factors in China Based on the Integration of the DMSP-OLS and NPP-VIIRS Nighttime Light Datasets

With the speedy growth of economic development, the imbalance of energy supply and demand pose a critical challenge for the energy security of our country. Meanwhile, the increasing and excessive energy consumption lead to the greenhouse effect and atmospheric pollution, greatly threatening the survival and development of human beings. This study integrated two nighttime light remote sensing datasets, namely Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data, to extend the temporal coverage of the study. Then, the distributions of China’s energy consumption from 1995 to 2016 at a 1-km resolution were estimated using different models and the spatiotemporal variations of energy consumption were explored on the basis of the best estimated results. Next, the factors influencing China’s energy intensity on the provincial level were investigated based on the spatial econometric model. The results show that: (1) The integrated nighttime light datasets can be successfully applied to estimate the dynamic changes of energy consumption. Moreover, the panel data model established in our research performed better than the quadratic polynomial model. (2) During the observation period, the energy consumption in China significantly increased, especially in the Yangtze River Delta, the Pearl River Delta, the Beijing–Tianjin–Hebei region, eastern coastal cities, and provincial capitals. (3) Different from the random spatial distribution pattern of energy consumption on the provincial level, the spatial distribution of energy consumption on the prefectural level has significant clusters, and its spatial agglomeration was strengthened year by year during the research period. (4) The spatial Durbin model (SDM) with a spatial fixed effect has been proved to be more suitable to explore the impact mechanism of China’s energy consumption. Among the four socio-economic factors, industrial structure has the greatest impact on the provincial energy intensity in China. Moreover, the changes in industrial structure and foreign direct investment (FDI) can not only influence the local energy intensity but also affect the energy intensity of the neighboring provinces.