What drives the change in China's provincial industrial carbon unlocking efficiency? Evidence from a geographically and temporally weighted regression model.

In this paper, we propose the concept of carbon unlocking efficiency based on carbon lock-in. Supported by the "Techno-Institutional Complex" theory, we measure the industrial carbon unlocking efficiency (ICUE) of 30 Chinese provinces and analyze its spatial and temporal jump probabilities through spatial Markov chains, and finally identify and discuss the influencing factors through the GTWR model. We found that the ICUE of each province in China follows a decreasing distribution from east to central to west, with Shanghai, Beijing, and Guangdong having the highest ICUEs among all provinces and cities; although the overall ICUE converges to a higher level in the long run, there is still a certain predatory effect of developed regions on less developed regions in the short term, and the intensification of market competition may adversely affect the growth of ICUE in the lagging regions. The results of GTWR show that factors such as energy use efficiency, FDI, and industrial enterprise size mainly promote ICUE growth, and energy structure mainly shows negative effects on ICUE of each province, while factors such as economic efficiency, R&D intensity, ownership structure, marketization level, share of high-tech industries, and industrial upgrading show obvious spatial heterogeneity, and different regions need to adopt different policy instruments for their strengths and weaknesses. These research results have important policy guidance implications for accelerating the process of industrial carbon unlocking in each region.

Coupling coordination degree and driving factors of new-type urbanization and low-carbon development in the Yangtze River Delta: based on nighttime light data.

The coordination relationship between new-type urbanization and urban low-carbon development under the goal of carbon neutrality has become a hot issue that needs to be focused on when formulating policies. Based on the estimation of urban CO2 emissions by night light data, this study used spatial autocorrelation, spatial Markov chain and geographically weighted regression model to measure the spatial correlation and spillover effects of the coupling coordination degree of two systems in the Yangtze River Delta urban agglomeration from 2005 to 2018 and analyzed the influencing factors. The results showed that (1) the coupling coordination degree showed an increasing trend, but the club effect was quite obvious, and the regional pattern was higher in southeast and lower in northwest; (2) the spatial spillover effect of coupling coordination degree is significant, which aggravates the long-term persistence of the imbalance pattern; (3) regional economic level, government fiscal regulation, and industrial upgrading are the main driving forces for the increase of coupling coordination degree, while over-concentration of population and low energy efficiency are the main obstacles. Finally, on the basis of these conclusions, we provide targeted policy planning suggestions for policy makers.

Enhanced dewaterability of waste-activated sludge with zero-valent iron-activated persulfate oxidation under mild hydrothermal conditions.

A novel technique to enhance sludge dewaterability with zero-valent iron-activated persulfate (ZVI/PDS) and hydrothermal treatment (HT) under mild temperature is proposed in this study. Key operating parameters were considered to study their influences on the dewaterability of sludge. Comparative studies of organic matter, especially extracellular polymeric substances (EPS), were analyzed carefully to reveal the mechanisms involved. The results indicated that the specific resistance to filtration declined by 86.72%, and the capillary suction time reduced by 72.35% compared with the raw sludge under optimal conditions of 100 mg/g TSS ZVI and 200 mg/g TSS PDS doses at 120 °C. Soluble protein and fulvic acid-like in EPS were the key components affecting the sludge dewatering performance. The disappearance of the peak in the amide III led to a decrease in hydrophilic functional groups, which helped to improve sludge dewaterability.

Impact of uncertainty on regional carbon peak paths: an analysis based on carbon emissions accounting, modeling, and driving factors.

Regional carbon emission paths have an important impact on the realization of China's carbon emission peak target. Due to the uncertainty of future development model, the change of carbon emissions will also face uncertainty, which will make achieving the peak target challenging. Taking Shandong, Henan, and Guangdong, three of China's most populous provinces, as examples, this study analyzed the impacts of uncertainties in carbon accounting principles, driving factors, and simulation mechanism on achieving the peak target. The results show that (1) under the baseline scenario, the accounting principles based on primary energy consumption and IPCC sector consumption will make the peaking time of Guangdong be evaluated as 2018 and 2030, respectively, and the simulation based on IPCC sector accounting will advance the peaking time of Shandong by at least 5 years, while Henan will be less affected. (2) When considering the impact of the energy structure, Guangdong and Henan are estimated to peak in 2011 and 2018, while without considering the impact of the energy structure, the peak in the two provinces may be after 2035. Energy structure has no effect on the estimation of peaking time for Shandong. In addition, the k value in the ridge regression method also has no effect on the peaking time for the three provinces; it only affects the simulations of annual carbon emissions. This study also presented the carbon emission trajectory under different scenarios; from the simulation results, environmental regulation measures such as accelerating industrial structure transformation and increasing energy consumption intensity may help to achieve the peak carbon emission target as soon as possible. It also suggests that uncertainty should be included in future carbon assessments to present a more complete carbon emission trajectory.

Spatio-Temporal Effects of Multi-Dimensional Urbanization on Carbon Emission Efficiency: Analysis Based on Panel Data of 283 Cities in China.

Under the influence of complex urbanization, improving the carbon emission efficiency (CEE) plays an important role in the construction of low-carbon cities in China. Based on the panel data of 283 prefectural-level cities in China from 2005 to 2017, this study evaluated the CEE by the US-SBM model, and explored the spatial agglomeration evolution characteristics of CEE from static and dynamic perspectives by integrating ESDA and Spatial Markov Chains. Then, the spatial heterogeneity of the impacts of multi-dimensional urbanization on CEE were analyzed by using the Geographically and Temporally Weighted Regression (GTWR). The results show that: (1) with the evolution of time, the CEE has a trend of gradual improvement, but the average is 0.4693; (2) from the perspective of spatial static agglomeration, the "hot spots" of CEE mainly concentrated in Shandong Peninsula, Pearl River Delta, and Chengdu-Chongqing urban agglomeration; The dynamic evolution of CEE gradually forms the phenomenon of "club convergence"; (3) urbanization of different dimensions shows spatial heterogeneity to CEE. The impact of economic urbanization in northern cities on CEE shows an inverted "U" shape, and the negative impact of spatial urbanization on CEE appears in the northwest and resource-based cities around Bohai Sea. Population and social urbanization have a positive promoting effect on CEE after 2010. These findings may help China to improve the level of CEE at the city level and provide a reference for low-carbon decision-making.

Exploring the Dependence and Influencing Factors of Carbon Emissions from the Perspective of Population Development.

Working towards sustainable population development is an important part of carbon mitigation efforts, and decoupling carbon emissions from population development has great significance for carbon mitigation. Based on the construction of a comprehensive population development index (PDI), this study adopts a decoupling model to explore the dependence between carbon emissions and PDI across 30 Chinese provinces from 2001 to 2017. Then, the stochastic impacts by regression on population, affluence and technology (STIRPAT) model is used to investigate the impact of population factors on carbon emissions. The results show that the decoupling relationship between carbon emissions and PDI has experienced a transformation from expansive negative coupling to expansive coupling and then to weak decoupling at the national level, while some provinces have experienced the same evolutionary process, but the decoupling state in most provinces is not ideal. Sending talent to western provinces and developing low-carbon supporting industries will accelerate carbon decoupling. At the national level, incorporating environmental protection into the existing education system as part of classroom teaching could contribute to carbon decoupling.