Carbon dioxide emission driving factors analysis and policy implications of Chinese cities: Combining geographically weighted regression with two-step cluster

Spatial-temporal characteristics analysis and ecological environment quality evaluation of forest health care bases in Yunnan, Guizhou and Sichuan provinces

As the forest health care in China is still in the early stage of development, the construction standards of forest health care base are not unified yet, resulting in large differences in the evaluation criterion for the ecological environment of forest health care bases. So, it is urgent to develop a new forest health care ecological environment quality assessment method. Yunnan, Guizhou and Sichuan provinces of China were selected as the study area, the previous 6 batches of 165 national forest health care pilot construction bases were selected as the main data source. This study explored the spatial and temporal distribution characteristics of forest health care bases in the study area using standard deviation ellipses, kernel density estimation method and cold and hot spot analysis. Furthermore, this study evaluated the ecological environment quality of the forest health care bases with a new ecological environment quality evaluation index model, which assembled Fraction Vegetation Coverage (FVC), Wetness (WET), Evapotranspiration (ET), Land Surface Temperature (LST) and Normalized Difference Bare Soil Index (NDBSI). The results are as follows: (1) the forest health bases in the study area are mainly located by the east of the Hu Line with a northeast-southwest distribution direction characteristics, and gradually expanded into a shape of "high in the east and low in the west, multi-point development". (2) the area with ecological environment quality in excellent, good and medium grade accounts for about 87.73 % in the study area, indicating that most of the study area is suitable for the construction of forest health care base. These results can provide a practical guidance for the further rational layout and balanced development of forest health care bases in the study area.

Spatio-temporal evolution characteristics of carbon emissions from road transportation in the mainland of China from 2006 to 2021

The leaping forward of the economy has promoted the rapid growth of road traffic demand, resulting in the carbon emissions of road traffic increasing significantly. It is well known that a one-size-fits-all emission reduction policy is not feasible. Therefore, conducting an investigation on the carbon emissions of all provincial-level regions within a country can assist the government in formulating carbon emission policies at a macro level tailored to different regions. In this study, the whole provincial-level administrative regions in the mainland of China were selected to quantify the carbon emissions of road traffic, and the carbon emissions from 2006 to 2021 were obtained by employing the top-down model. What's more, spatiotemporal characteristics of road transportation carbon emissions in those regions were explored based on Moran's I spatial autocorrelation method. In addition, the LMDI model was constructed based on five driving factors, namely energy intensity, energy consumption intensity, industrial scale, economic development, and population size, and the decomposition analysis of driving factors is carried out. The results show that carbon emissions from road traffic in all provincial regions showed an overall rising trend in the research period, with an average annual growth rate of 11.83 %. The distribution of road transportation carbon emissions exhibited an east-high, west-low distribution, with significantly higher emissions in the eastern and coastal regions compared to inland areas, additionally, China's seven geographical regions showed an initial rapid increase in carbon emissions followed by a stable growth trend. Secondly, five types of spatial clustering were identified of carbon emissions within provincial regions. Thirdly, the impacts of energy intensity and industrial scale were detrimental to road transportation carbon emissions, whereas economic development, energy consumption intensity, and population size had contrasting effects. Implications according to the above conclusions were put forward, aiming to provide guidance for the sustainable development of road transportation and expediting the achievement of the "carbon peaking and carbon neutrality" objective.

Research on the relationship between common metabolic syndrome and meteorological factors in Wuhu, a subtropical humid city of China

As climate conditions deteriorate, human health faces a broader range of threats. This study aimed to determine the risk of death from metabolic syndrome (MetS) due to meteorological factors. We collected daily data from 2014 to 2020 in Wuhu City, including meteorological factors, environmental pollutants and death data of common MetS (hypertension, hyperlipidemia and diabetes), as well as a total number of 15,272 MetS deaths. To examine the relationship between meteorological factors, air pollutants, and MetS mortality, we used a generalized additive model (GAM) combined with a distributed delay nonlinear model (DLNM) for time series analysis. The relationship between the above factors and death outcomes was preliminarily evaluated using Spearman analysis and structural equation modeling (SEM). As per out discovery, diurnal temperature range (DTR) and daily mean temperature (T mean) increased the MetS mortality risk notably. The ultra low DTR raised the MetS mortality risk upon the general people, with the highest RR value of 1.033 (95% CI: 1.002, 1.065) at lag day 14. In addition, T mean was also significantly associated with MetS death. The highest risk of ultra low and ultra high T mean occured on the same day (lag 14), RR values were 1.043 (95% CI: 1.010, 1.077) and 1.032 (95% CI: 1.003, 1.061) respectively. Stratified analysis's result showed lower DTR had a more pronounced effect on women and the elderly, and ultra low and high T mean was a risk factor for MetS mortality in women and men. The elderly need to take extra note of temperature changes, and different levels of T mean will increase the risk of death. In warm seasons, ultra high RH and T mean can increase the mortality rate of MetS patients.

Exploring the spatiotemporal pattern evolution of carbon emissions and air pollution in Chinese cities

Based on data from 335 cities in China, this study employs the standard deviation ellipse method to portray unbalanced and differential spatiotemporal evolution patterns of environmental emissions and socioeconomic elements. A logarithmic mean Divisia index analysis and in-depth discussion are carried out to disclose the main driving factors and underlying reasons for the differences. Decoupling trends exist among carbon emissions, gross domestic product (GDP) and population in terms of their gravity center migrations. The standard deviation ellipse direction of carbon emissions gradually changed from 'northeast‒southwest' to 'northwest‒southeast', and the standard deviation ellipse areas of carbon emissions and air pollution continuously expanded over time; at the same time, that of GDP contracted. Economic growth has always been the main driver of carbon emissions and air pollution nationally, but its role has weakened. Moreover, decreases in the energy intensity and carbon and pollution intensities are the main factors contributing to emissions reductions. Differentiated spatiotemporal economic structure evolution, regional heterogeneities in the energy intensity and efficiency, and cross-region power energy transmissions are identified as the underlying reasons for the unbalanced spatiotemporal patterns of the environmental emissions and socioeconomic elements. Based on these findings, policy suggestions can be made to address the imbalances and promote carbon mitigation, air quality improvement and high-quality social-economic development at the city level.

Spatial-temporal evolution patterns and drivers of PM2.5 chemical fraction concentrations in China over the past 20 years

The quantitative assessment of the spatial and temporal variability and drivers of fine particulate matter (PM2.5) fraction concentrations are important for pollution control and public health preservation in China. In this study, we investigated the spatial temporal variation of PM2.5 chemical component based on the PM2.5 chemical component datasets from 2000 to 2019 and revealed the driving forces of the differences in the spatial distribution using geodetector model (GD), multi-scale geographically weighted regression model (MGWR), and a two-step clustering approach. The results show that: the PM2.5 chemical fraction concentrations show a trend of first increasing (2000-2007) and then decreasing (2007-2019). From 2000 to 2019, the change rates of PM2.5, organic matter (OM), black carbon (BC), sulfates (SO2- 4), ammonium (NH+ 4), and nitrates (NO- 3) were -0.59, -0.23, -0.07, -0.15, -0.02, and 0.04μg/m3/yr in the entirety of China. The secondary aerosol (i.e., SO2- 4, NO- 3, and NH+ 4; SNA) had the highest fraction in PM2.5 concentrations (55.6-68.1% in different provinces), followed by OM and BC. Spatially, North, Central, and East China are the regions with the highest PM2.5 chemical component concentrations in China; meanwhile, they are also the regions with the most significant decrease in PM2.5 chemical fraction concentrations. The GD and MGWR model shows that among all variables, the number of enterprises, disposable income, private car ownership, and the share of secondary industry non-linearly enhance the differences in the spatial distribution of PM2.5 component concentrations. Electricity consumption has the strongest influence on NH+ 4 emissions in Northwest China and BC and OM emissions in Northeast China.

Black carbon pollution in China from 2001 to 2019: Patterns, trends, and drivers

Based on a near real-time 10 km × 10 km resolution black carbon (BC) concentration dataset, this study investigated the spatial patterns, trend variations, and drivers of BC concentrations in China from 2001 to 2019 with spatial analysis, trend analysis, hotspot clustering, and multiscale geographically weighted regression (MGWR). The results indicate that Beijing-Tianjin-Hebei, the Chengdu-Chongqing agglomeration, Pearl River Delta, and East China Plain were the hotspot centers of BC concentration in China. From 2001 to 2019, the average rate of decline in BC concentrations across China was 0.36 μg/m³/year (p < 0.001), with BC concentrations peaking around 2006 and sustaining a decline for the next decade or so. The rate of BC decline was higher in Central, North, and East China than in other regions. The MGWR model revealed the spatial heterogeneity of the influences of different drivers. A number of enterprises had significant effects on BC in East, North, and Southwest China; coal production had strong effects on BC in Southwest and East China; electricity consumption had better effects on BC in Northeast, Northwest, and East China than in other regions; the ratio of secondary industries had the greatest effects on BC in North and Southwest China; and CO₂ emissions had the strongest effects on BC in East and North China. Meanwhile, the reduction of BC emissions from the industrial sector was the dominant factor in the decrease of BC concentration in China. These findings provide references and policy prescriptions for how cities in different regions can reduce BC emissions.

Developing personas for live streaming commerce platforms with user survey data

Live streaming commerce has emerged as a novel form of online marketing that offers live streaming commerce platforms a means of meeting different user groups' needs. The objective of this article is to examine the effects of age and gender on live streaming commerce platform usage and investigate user characteristics of these platforms in China. This study adopted a data-driven persona construction method combining quantitative and qualitative methods through the use of survey and interview. The survey involved 506 participants (age range = 19-70), and the interview involved 12 participants. The survey findings showed that age significantly affected users' livestream platform usage, while gender did not. Younger users had higher device proficiency and operation numbers. With more trust and device use, older users used the platforms later in the day than younger users. Interview findings revealed that gender affected users' motivations and value focus. Women tended to use the platforms as a means of entertainment. Women valued service quality and enjoyment more, while men focused on the accuracy of product information more. Four personas with significant differences were then constructed: Dedicated, Dependent, Active and Lurker. Their various needs, motivations and behavior patterns can be considered by designers to elevate the interaction of live streaming commerce platforms.

The heterogeneous drivers of CO2 emissions in China's two major economic belts: new evidence from spatio-temporal analysis

CO2 emissions have become increasingly prominent in China, and the primary emitters are economic belts that are spread throughout China. Two major economic belts, i.e., the Yangtze River Economic Belt (YTREB) and the Yellow River Economic Belt (YREB). Combined with stochastic impacts by regression on population, affluence and technology model, the spatial Durbin model under the space-and-time fixed effect and the Geographical and Time-Weighted Regression are employed to explore the spatio-temporal distribution characteristics and heterogeneous drivers of CO2 emissions in the two economic belts. The results are as follows. First, CO2 emissions exhibit obvious spatial correlation features in the YREB, but no such obvious spatial correlation is found in the YRETB. Second, in the YREB, the magnitude of the total influencing factors on CO2 emissions follows an order where affluence (A) is the biggest driver, followed by energy intensity (EI), technology (TEC) and openness (OP), while the biggest driver in the YRETB is industrial structure supererogation (ISS), followed by population (P), energy intensity (EI), and affluence (A). Both direct and spatial spillover effects of the drivers are observed in the two economic belts. Third, the CO2 emissions show a notable temporal lag effect in the YREB, but not in the YRETB. Fourth, the effects of the CO2 emission drivers illustrate significant spatio-temporal heterogeneity in the two economic belts.

Impact of Internet Development on Carbon Emissions in Jiangsu, China

Based on STIRPAT and panel threshold models, this study empirically tested the impact of Internet development on carbon emissions using panel data of Jiangsu Province from 2007 to 2020. The results showed that the carbon emissions intensity of the Internet development level had a significant promotion effect, while the carbon emissions intensity of technological progress showed a significant inhibition effect, but this inhibition effect is less than the promotion effect brought about by internet development. Considering the threshold effect, the development of the Internet had a double-threshold effect on carbon emissions in northern and central Jiangsu. Jiangsu Province should further accelerate the pace of Internet development and cross the threshold value as soon as possible. Finally, this study constructed a prediction model of emissions reduction to predict the future emissions reduction potential of Jiangsu Province and found that there was still much room for improvement regarding carbon emissions reduction in Jiangsu Province.

Identifying spatial relations of industrial carbon emissions among provinces of China: evidence from unsupervised clustering algorithms

Reducing the total carbon emissions of modern industry is of great significance for China to achieve the carbon peak mission. The MD-SNA spatial correlation measure methodology was innovatively proposed in this paper, which was based on the clustering algorithm of similarity measure. Furthermore, the social network analysis (SNA) method was incorporated to explore the spatial relationship of provincial industrial carbon emissions. The GINI coefficient, Theil index (GE₀), and mean of logarithmic deviation (GE₁) were used to measure the regional differences of China's industrial carbon emissions. More specifically, we adopted a combined tactic of spatial difference and spatial correlation frameworks. The primary objective of the proposed methodology is to empirically investigate the structural characteristics and spatial relations of different provinces. The results of the case study are as follows. First, the regional industrial carbon emission intensity was unbalanced, among which energy-rich provinces and eastern developed provinces were relatively strong. Second, Beijing, Shandong, Shaanxi, Henan, Sichuan, and Xinjiang were located at the center of the spatial network of industrial carbon emissions. Third, our work clarified the node attributes and different functions of provinces. More than half of the core provinces belonged to the primary beneficial block, which was in the central position of spatial correlation network. The conclusion can help policymakers clarify the overall industrial sector spatial pattern and provinces' roles and functions.

Monitoring spatiotemporal characteristics of land-use carbon emissions and their driving mechanisms in the Yellow River Delta: A grid-scale analysis

Comprehensive and accurate grasp of land-use carbon emissions (LCE) level and its driving mechanism is key to success in China's pursuit of low-carbon development, and it is also the scientific basis for the formulation and implementation of regional carbon emissions strategies. Based on fossil fuel carbon emissions raster data (published by the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) platform) and land use data, this manuscript selects the Yellow River Delta as the study area and uses an improved LCE measurement model, exploratory spatial data analysis, multiscale geographical weighting regression (MGWR), and other models to explore the spatiotemporal heterogeneity and driving mechanisms of LCE at the grid level. The results showed the following: ① The total amount of LCE in the study area continued to increase from 2000 to 2019, the growth rate decreased, but the peak of LCE had not yet been reached. ② The LCE of the study area showed a significant positive global autocorrelation. The H-H aggregation region showed a relatively stable spatial distribution range; the L-L aggregation region showed wide distribution characteristics that covered the entire study area; and the aggregation regions of H-L and L-H, which have not yet reached the scale. ③ At the global dimension, the mean correlation coefficients between LCE and driving factors (net primary productivity (NPP), nighttime light (NTL), and population density (PD)) from 2000 to 2019 were -0.11, 0.28, and 0.12; at the local dimension, the strength (from strong to weak) of the effect of each factor on LCE was PD, NTL, NPP (2000) and NTL, PD, NPP (2019). The research results provide a scientific basis and basic guarantee for the development, and implementation of regional carbon emission strategies.

Relationship between meteorological factors and mortality from respiratory diseases in a subtropical humid region along the Yangtze River in China

As the health impacts of climate change take on a more serious form, this study for the first time investigates the effect of meteorological factors on the risk of death from respiratory diseases (RD) in Wuhu, a representative city along the Yangtze River in subtropical humid region. Daily meteorological element data and RD deaths in Wuhu City were collected from 2014 to 2020. Time series analysis was conducted using distributed lagged nonlinear model (DLNM) combined with generalized additive model (GAM), and stratified by age and gender. In 7 years, a total of 8016 RD death cases were collected in Wuhu, China. The results demonstrated that the maximum impacts of short-term exposure to exceedingly low temperatures mean (Tmean) were at lag 9, with the maximum relative risk (RR) of 1.044 (lag 1, 95% CI: 1.001, 1.098). The risk of exceedingly high Tmean reached its maximum at lag 0 (RR = 1.070, 95% CI: 1.018, 1.125). Low relative humidity (RH) was negatively associated with the risk of RD death, with the lowest RR values occurring at lag 12 (RR = 0.987, 95% CI: 0.975, 0.999). No significant correlation was found for diurnal temperature range (DTR). Stratified analysis showed that Tmean exposure remained statistically significant for male, female and elderly, while RH and DTR only seemed to increase the mortality risk in the young. In a word, short-term exposure to extreme temperatures may increase the RD mortality risk in the population, and young people needed to be aware that exposure to exceedingly high RH and DTR also increased the risk.

Spatiotemporal Dynamics of Direct Carbon Emission and Policy Implication of Energy Transition for China’s Residential Consumption Sector by the Methods of Social Network Analysis and Geographically Weighted Regression

As China’s second largest energy-use sector, residential consumption has a great potential for carbon dioxide (CO2) reduction and energy saving or transition. Thus, here, using the methods of social network analysis (SNA) and geographically weighted regression (GWR), we investigated the spatiotemporal evolution characteristics of China’s residential CO2 emissions (RCEs) from direct energy use and proposed some policy suggestions for regional energy transition. (1) From 2000 to 2019, the total direct RCEs rose from 396.32 Mt to 1411.69 Mt; the consumption of electricity and coal were the primary sources. Controlling coal consumption and increasing the proportion of electricity generated from renewable energy should be the effective way of energy transition. (2) The spatial associations of direct RCEs show an obvious spatial network structure and the number of associations is increasing. Provinces with a higher level of economic development (Beijing, Shanghai, and Jiangsu) were at the center of the network and classified as the net beneficiary cluster in 2019. These provinces should be the priority areas of energy transition. (3) The net spillover cluster (Yunnan, Shanxi, Xinjiang, Gansu, Qinghai, Guizhou) is an important area to develop clean energy. People in this cluster should be encouraged to use more renewable energy. (4) GDP and per capita energy consumption had a significant positive influence on the growth of direct RCEs. Therefore, the national economy should grow healthily and sustainably to provide a favorable economic environment for energy transition. Meanwhile, residential consumption patterns should be greener to promote the use of clean energy.

Management implications of spatial-temporal variations of net anthropogenic nitrogen inputs (NANI) in the Yellow River Basin

It is an important content of environment management to accurately identify the time change and spatial distribution of net anthropogenic nitrogen inputs (NANI) in the river basin. In order to develop a unified management and diverse control strategy that fits the characteristics of the basin, this study establishes the NANI-S model combining the NANI model with the spatial autocorrelation analysis method, which is a quantification-analysis-control process, and takes the 70 prefecture-cities in the Yellow River Basin (YRB) as the study area. The result shows that (1) the NANI of YRB increased first and then decreased with an average NANI value of 6787.59 kg/(km²·a), showing that the overall N pollution situation of the YRB shows a trend of improvement in nitrogen (N) fertilizer input as the main source, and the average contribution rate was 47.45%. (2) There were obvious spatial differences in the NANI in the YRB because the global Moran's I fluctuated between 0.67 and 0.78. Cities with high NANI clustered in the middle and lower reaches, while low NANI clustered in the upper reaches. (3) Improving fertilizer utilization rate and industrial and domestic sewage treatment capacity was the key point of N control. Based on the results, practical policy recommendations for water pollution management were constructed, which provides a scientific basis for pollution prevention and high-quality development in the basin. In addition, this analysis method can also be applied to other basin N management studies.

Carbon Emissions Estimation and Spatiotemporal Analysis of China at City Level Based on Multi-Dimensional Data and Machine Learning

Carbon emissions caused by the massive consumption of energy have brought enormous pressure on the Chinese government. Accurately and rapidly characterizing the spatiotemporal characteristics of Chinese city-level carbon emissions is crucial for policy decision making. Based on multi-dimensional data, including nighttime light (NTL) data, land use (LU) data, land surface temperature (LST) data, and added-value secondary industry (AVSI) data, a deep neural network ensemble (DNNE) model was built to analyze the nonlinear relationship between multi-dimensional data and province-level carbon emission statistics (CES) data. The city-level carbon emissions data were estimated, and the spatiotemporal characteristics were analyzed. As compared to the energy statistics released by partial cities, the results showed that the DNNE model based on multi-dimensional data could well estimate city-level carbon emissions data. In addition, according to a linear trend analysis and standard deviational ellipse (SDE) analysis of China from 2001 to 2019, we concluded that the spatiotemporal changes in carbon emissions at the city level were in accordance with the development of China’s economy. Furthermore, the results can provide a useful reference for the scientific formulation, implementation, and evaluation of carbon emissions reduction policies.

Evaluating the nexus between ongoing and increasing urbanization and carbon emission: a study of ARDL-bound testing approach

The growing urbanization has created a substantial economic imbalance between the urban and rural households in the world's emerging economies and put a significant effect on carbon dioxide emissions. Simultaneously, many researchers have grown concerned by the significant consequences of urbanization on carbon emissions. In current research, we make an effort in Pakistan to investigate how urbanization affects the carbon emissions. In order to attain the intended goals of long run and short run investigation, we employed the most appropriate method of auto-regressive distributed lag model for time series data-set, while the vector error correction model, on the other hand, was employed to investigate causation. The estimated findings of the auto-regressive distributed lag model supported the association amongst the model's selected variables. In long and short run, the estimated findings approved that as the level of urbanization rises, so does the carbon emissions. Furthermore, the estimated results of the vector error correction model acknowledged the validity of short run unidirectional causal relationship from urbanization towards carbon emissions and from carbon emission towards energy consumption, as well as in short run, where economic growth one-way Granger generates the carbon emission. To summarize, based on the current findings, that reflects the significance of the underlined factors. The study recommends that government should prioritize the development of energy efficient and environment friendly strategies with respect to fast growing urbanization to control the carbon emission.

Spatio-Temporal Heterogeneity of Carbon Emissions and Its Key Influencing Factors in the Yellow River Economic Belt of China from 2006 to 2019

The Yellow River Economic Belt (YREB) performs an essential function in the low-carbon development of China as an important ecological protection barrier, and it is of great importance to identify its spatio-temporal heterogeneity and key influencing factors. In this study, we propose a comprehensively empirical framework to conduct this issue. The STIRPAT model was applied to determine the influencing factors of carbon emissions in the YREB from 2006 to 2019. The results show that the carbon emissions in the YREB had significant clustering characteristics in the spatial auto-correlation analysis. In addition, the estimation results of the spatial panel analysis demonstrate that the carbon emissions showed a distinct spatial lag effect and temporal lag effect. Moreover, the three traditional factors including population, affluence, technology are identified as the key influencing factors of carbon emissions in the YREB of China. Furthermore, the spatio-temporal heterogeneity is illustrated vividly by employing the GTWR-STIRPAT model. Finally, policy implications are provided to respond to the demand for low-carbon development.

Carbon Emission Efficiency Network: Evolutionary Game and Sensitivity Analysis between Differentiated Efficiency Groups and Local Governments

With its proposal of the “double carbon” (peak carbon dioxide emissions and carbon neutralization) goal, China has entered a new stage in creating an ecological civilization and achieving sustainable development. Based on the formation and evolution mechanism of the carbon emission efficiency network, in this study, a trilateral evolutionary game model—including efficiency groups (high- and low-efficiency groups) and local governments—was constructed, in an attempt to discuss the conditions needed for different players and trilateral interconnected systems to implement balanced and stable strategies. Furthermore, the sensitivity of the participants’ evolutionary trajectories toward factors such as the initial strategy ratio, transition cost, and network capital were tested via a system simulation. The main conclusions were as follows: (1) Efficiency groups form a virtuous circle when the initial proportion of the participants’ strategies reaches a certain threshold, and converge into a stable “win–win” state. Under these circumstances, high-efficiency groups tend to give full play to their efficiency advantages in terms of carbon emission reduction and green development, while low-efficiency groups tend to choose green transformation and accept the spillover effect from high-efficiency groups. (2) When efficiency groups achieve a “win–win” state or form good self-management, local governments move from active supervision to a passive supervision strategy in order to reduce supervision costs. (3) While different initial strategy proportions do not affect the stable convergence point of the evolutionary system, they have a differentiated impact on the convergence speed of the players. Under the condition of a low initial strategy ratio, transformation costs can reduce the green transformation enthusiasm of inefficient groups, while network capital can enhance the green transformation willingness of inefficient groups.

Convergence or divergence? The effects of economic openness on low-carbon innovation in Chinese manufacturing industry

Low-carbon innovation can address both economic and environmental concerns; patterns of low-carbon innovation convergence can determine the effectiveness of mitigating the adverse consequences of climate change. Considering that economic openness has a huge impact on the development of innovation capability, this paper uses a conditional β convergence model to examine the convergence of low-carbon innovation in Chinese manufacturing industry and its relationship with economic openness. We incorporate the spatial spillover effect into the convergence function by constructing spatial error model, spatial lag model, and spatial Durbin model. Based on a panel data set of 30 Chinese provinces over the period 2004-2016, the results show that low-carbon innovation in Chinese manufacturing industry has a strong feature of conditional β convergence. The convergence rate of low-carbon innovation is slightly slowed down by economic openness, and the main reason is that the spillover effect is weak and the convergence rate is slow in lower open areas, so the convergence rate of the whole country is slowed down by that of the lower open areas. Although the economic openness in adjacent areas can contribute to the development of local innovation ability, but generally speaking, economic openness in local areas takes a stronger effect in promoting the convergence of low-carbon innovation than that in adjacent areas. The findings have important policy implications as they suggest the need for a more equal degree of economic openness among Chinese provinces to speed up the convergence of low-carbon innovation.

Spatial-temporal evolution and influencing factors of total factor productivity in China's logistics industry under low-carbon constraints

Behind the rapid development of China's logistics industry, there are problems of high energy consumption and high pollution. Under the dual constraints of resources and environment, promoting the low-carbon transformation of the logistics industry is the key to achieving sustainable development of the logistics industry. This paper applies the epsilon-based measure (EBM) model which considers undesirable output and global Malmquist-Luenberger (GML) index to measure the logistics efficiency under the low-carbon constraints of 30 provinces in China from 2005 to 2017, that is, the green total factor productivity (GTFP), and characterizes its temporal and spatial evolution characteristics through visualization and spatial analysis methods. Then, this paper uses the geographically weighted regression (GWR) model to analyze the influence of industrial agglomeration level, informatization level, foreign direct investment, logistics energy intensity, traffic network density, and technological innovation capability on the GTFP of the logistics industry. The findings of this paper show that (1) during the inspection period, the overall average GTFP of the logistics industry was 0.992, which did not reach the effective level, and the spatial differentiation showed that the average GTFP of eastern was greater than that of in central, and that of in central was greater than that of in western. (2) The GTFP of the logistics industry has experienced an alternating process of rising and falling in time, with large fluctuations. Also, in terms of spatial dimension, there is a trend that high-level areas gradually gather to the southeast, and there is significant spatial autocorrelation. (3) For the logistics industry, high-efficiency areas and high-output areas show significant spatial homogeneity. (4) The estimation results of the GWR show that the direction and intensity of the multi-dimensional driving factors on the GTFP of the logistics industry are different in different regions, showing obvious spatial non-stationary characteristics.

Spatial Heterogeneity of Factors Influencing CO2 Emissions in China’s High-Energy-Intensive Industries

In recent years, China has overtaken the United States as the world’s largest carbon dioxide (CO2) emitter. CO2 emissions from high-energy-intensive industries account for more than three-quarters of the total industrial carbon dioxide emissions. Therefore, it is important to enhance our understanding of the main factors affecting carbon dioxide emissions in high-energy-intensive industries. In this paper, we firstly explore the main factors affecting CO2 emissions in high-energy-intensive industries, including industrial structure, per capita gross domestic product (GDP), population, technological progress and foreign direct investment. To achieve this, we rely on exploratory regression combined with the threshold criteria. Secondly, a geographically weighted regression model is employed to explore local-spatial heterogeneity, capturing the spatial variations of the regression parameters across the Chinese provinces. The results show that the growth of per capita GDP and population increases CO2 emissions; by contrast, the growth of the services sector’s share in China’s gross domestic product could cause a decrease in CO2 emissions. Effects of technological progress on CO2 emissions in high-energy-intensive industries are negative in 2007 and 2013, whereas the coefficient is positive in 2018. Throughout the study period, regression coefficients of foreign direct investment are positive. This paper provides valuable insights into the relationship between driving factors and CO2 emissions, and also gives provides empirical support for local governments to mitigate CO2 emissions.

The construction and examination of social vulnerability and its effects on PM2.5 globally: combining spatial econometric modeling and geographically weighted regression

Fine particulate matter (PM2.5) is of widespread concern, as it poses a serious impact on economic development and human health. Although the influence of socioeconomic factors on PM2.5 has been studied, the constitution and the effect analysis of social vulnerability to PM2.5 remain unclear. In this study, a comprehensive theoretical framework with appropriate indicators for social vulnerability to PM2.5 was constructed. Using spatial autocorrelation analysis, a positive global spatial autocorrelation and notable local spatial cluster relationships were identified. Spatial econometric modeling and geographically weighted regression modeling were performed to explore the cause-effect relationship of social vulnerability to PM2.5. The spatial error model indicated that population and education inequality in the sensitivity dimension caused a significant positive impact on PM2.5, and biocapacity and social governance in the capacity dimension strongly contributed to the decrease of PM2.5 globally. The geographically weighted regression model revealed spatial heterogeneity in the effects of the social vulnerability variables on PM2.5 among countries. These empirical results can provide policymakers with a new perspective on social vulnerability as it relates to PM2.5 governance and targeted environmental pollution management.

Impact of population growth and land use and land cover (LULC) changes on water quality in tourism-dependent economies using a geographically weighted regression approach

This paper aims to assess the influence of land use and land cover (LULC) indicators and population density on water quality parameters during dry and rainy seasons in a tourism area in Indonesia. This study applies least squares regression (OLS) and Pearson correlation analysis to see the relationship among factors, and all LULC and population density were significantly correlated with most of water quality parameter with P values of 0.01 and 0.05. For example, DO shows high correlation with population density, farm, and built-up in dry season; however, each observation point has different percentages of LULC and population density. The concentration value should be different over space since watershed characteristics and pollutions sources are not the same in the diverse locations. The geographically weighted regression (GWR) analyze the spatially varying relationships among population density, LULC categories (i.e., built-up areas, rice fields, farms, and forests), and 11 water quality indicators across three selected rivers (Ayung, Badung, and Mati) with different levels of tourism urbanization in Bali Province, Indonesia. The results explore that compared with OLS estimates, GWR performed well in terms of their R2 values and the Akaike information criterion (AIC) in all the parameters and seasons. Further, the findings exhibit population density as a critical indicator having a highly significant association with BOD and E. Coli parameters. Moreover, the built-up area has correlated positively to the water quality parameters (Ni, Pb, KMnO4 and TSS). The parameter DO is associated negatively with the built-up area, which indicates increasing built-up area tends to deteriorate the water quality. Hence, our findings can be used as input to provide a reference to the local governments and stakeholders for issuing policy on water and LULC for achieving a sustainable water environment in this region.

Improved enrichment factor model for correcting and predicting the evaluation of heavy metals in sediments

As the most widely used method for evaluating heavy metals (HMs) in soil or sediment, the enrichment factor (EF) is prone to bias and even yields misleading assessment results for HM pollution due to data uncertainties, lack of local background values and a failure to assess the comprehensive pollution of multiple HMs. Here, we developed an improved EF model integrating stochastic mathematical methods and geochemical baselines (GBs). First, GBs were obtained using the relative cumulative frequency distribution method. The probability that each HM belongs to each enrichment degree was then quantified based on the probability density function deduced from the maximum entropy method. Furthermore, we defined a synthetic index to reveal the probability that multiple HMs belongs to comprehensive enrichment degree considering the weight of each HM. Finally, the enrichment category for each HM and multiple HMs were determined following the first-order moment principle. The improved EF model was successfully applied to evaluate and predict the HM pollution in sediments collected from Poyang Lake, the largest freshwater lake in China. Slight enrichment (1.88) of multiple HMs was found in sediments from Poyang Lake, characterized by a pronounced probability (0.35) to deteriorate to the "moderate enrichment" category. Among the different HMs, Cd requires more attention considering its dominant contribution (0.51) to the comprehensive pollution and high probability (0.65) for deterioration. Otherwise, assessment results employing the improved EF model agree with the spatial patterns of HM concentrations based on spatial autocorrelation analysis and source apportionment using Pb isotopic signatures and principal component analysis. Compared with the conventional EF method, the assessment results of the improved EF model were more accurate, comprehensive and reliable. In conclusion, the improved EF model has a better capability of evaluating and predicting HM enrichment in sediments and can be helpful for optimizing control measures for HM pollution.

Study on the Spatial Association and Influencing Factors of Carbon Emissions from the Chinese Construction Industry

As the world’s largest carbon emitter, China is under enormous pressure to decrease carbon emissions. With the economic development in recent years, China has increased its investment in infrastructure, and the construction industry has become an essential source of carbon emissions. Using the social network analysis (SNA) methodology, this article analyzes the evolutionary characteristics of the spatial correlation network for carbon emissions in the construction industry from 2003–2017 and its affecting factors. The results of the empirical analysis in this paper are: (1) the spatial association of carbon emissions in Chinese inter-provincial construction industry shows an intuitive network layout and the spatial network has gradually stabilized since 2014; (2) according to the results of degree centrality, betweenness centrality and closeness centrality, it can be concluded that the regions with higher level of association with other provinces are the central and the eastern regions (Henan, Hubei, Hunan, Guangdong, Jiangsu, etc.) and Xinjiang; the linkage of construction-related carbon emissions was mainly achieved through the regions of Henan, Anhui, Shanxi, Hebei, Guangdong, and Inner Mongolia; the regions with higher level of construction industry development (Jiangsu, Henan, Hunan, Guangdong, etc.) are more closely associated with other provinces; (3) geographical proximity and reduction of difference in energy intensity and in industrial structure have substantial positive effects on the carbon emission association of the construction industry. Finally, based on the research results, this article proposes corresponding policy recommendations.

Normalized difference vegetation index as the dominant predicting factor of groundwater recharge in phreatic aquifers: case studies across Iran

The estimation of long-term groundwater recharge rate ([Formula: see text]) is a pre-requisite for efficient management of groundwater resources, especially for arid and semi-arid regions. Precise estimation of [Formula: see text] is probably the most difficult factor of all measurements in the evaluation of GW resources, particularly in semi-arid regions in which the recharge rate is typically small and/or regions with scarce hydrogeological data. The main objective of this study is to find and assess the predicting factors of [Formula: see text] at an aquifer scale. For this purpose, 325 Iran's phreatic aquifers (61% of Iran's aquifers) were selected based on the data availability and the effect of eight predicting factors were assessed on [Formula: see text] estimation. The predicting factors considered include Normalized Difference Vegetation Index (NDVI), mean annual temperature ([Formula: see text]), the ratio of precipitation to potential evapotranspiration ([Formula: see text]), drainage density ([Formula: see text]), mean annual specific discharge ([Formula: see text]), Mean Slope ([Formula: see text]), Soil Moisture ([Formula: see text]), and population density ([Formula: see text]). The local and global Moran's I index, geographically weighted regression (GWR), and two-step cluster analysis served to support the spatial analysis of the results. The eight predicting factors considered are positively correlated to [Formula: see text] and the NDVI has the greatest influence followed by the [Formula: see text] and [Formula: see text]. In the regression model, NDVI solely explained 71% of the variation in [Formula: see text], while other drivers have only a minor modification (3.6%). The results of this study provide new insight into the complex interrelationship between [Formula: see text] and vegetation density indicated by the NDVI. The findings of this study can help in better estimation of [Formula: see text] especially for the phreatic aquifers that the hydrogeological ground-data requisite for establishing models are scarce.

A multi-faceted, location-specific assessment of land degradation threats to peri-urban agriculture at a traditional grain base in northeastern China

Urbanization-induced cultivated land degradation can hamper the ability of peri-urban agriculture (PUA) to deliver clean food and agroecosystem services. Detailed geo-information about which cultivated lands are being influenced by urbanization will be important to designing future measures for the conservation of PUA. This information will be especially relevant for traditional grain bases because PUA is often underappreciated in these regions. For this reason, we performed a multi-faceted and location-specific assessment, including soil pollution, soil fertility, basic tillage conditions and land fragmentation, of cultivated land in a rural-urban transition zone outside of a city in northeast China. We also illustrated the combined risks in different urbanized environments via GIS-based two-step spatial clustering. The results indicated that, in general, cultivated lands were more polluted and fragmented, as well as less fertile and tillable, the closer they were to the urban area. Most of the affected cultivated lands were located within 8 km of the urban periphery. Furthermore, certain urban environments exposed the surrounding cultivated lands to specific degradation in relation to different combined risks. PUA in long-standing industrial areas mainly faced risks of polluted agricultural production, underutilization and impaired landscape ecological security (LES), whereas cultivated lands close to a recently developed residential area were characterized by risks of supplying service disruption, unsustainable agricultural production, underutilization and impaired LES. The present study highlighted that PUA associated with traditional grain bases must be preserved to enhance urban sustainability and resilience, and suggests that measures which can adapt to multi-faceted local degradation issues will be the most effective protection for peri-urban areas. Furthermore, the results also suggest that multi-functional and profitable agriculture will contribute to breaking the vicious circle of land degradation in peri-urban cultivated areas of traditional grain bases.

Who is a good neighbor? Analysis of frontrunner cities with comparative advantages in low-carbon development

A well-developed economy and low-carbon emission intensity are important characteristics of low-carbon cities; they also represent important tasks for achieving global climate change mitigation goals. It is seldom discussed, however, how we should identify frontrunner cities from which low-carbon development experiences can be gleaned and then implemented in neighboring cities. This study, therefore, proposed a simple indicator-the "good neighbor index"-to identify frontrunner cities in low-carbon transformation based on economic and emission performance. Based on this indicator, we identified "good neighbors" in static and dynamic views for China. The results showed that the static good neighbors in 2015 were mostly large cities with higher incomes and better industrial structures whereas the dynamic neighbors achieved better economic growth and emission reductions from 2005 to 2015, though their economic and emissions statuses were generally worse. The good neighbor list is not consistent with the list of national low-carbon pilot cities, which has largely overlooked the experiences of some fast-growing cities. These results have policy implications for the Chinese government in terms of promoting the low-carbon transformation of cities. The study can also provide a reference for other countries in addressing climate change at the city level.

Examining the spatiotemporal variations and inequality of China's provincial CO2 emissions

Tremendous energy consumption appears as rapid economic development, leading to large amount of CO₂ emissions. Although plentiful studies have been made into the driving factors of CO₂ emissions, the existing literatures that take the spatial differences and temporal changes into consideration are few. Therefore, this study first analyzes the variations of total CO₂ emissions' spatial distribution from 2008 to 2017 and present the changes of driving factors, finding significant spatial autocorrelation in CO₂ emissions at the province level, and that urbanization rate, per capita GDP and per capita CO₂ emissions increased significantly, but energy consumption structure and trade openness decreased. We then compared the effects of different factors affecting CO₂ emissions, using classic linear regression model, panel data model, and the geographically weighted regression (GWR) model, and the three models roughly agree on the effects of factors. The GWR model considering spatial heterogeneity provides more detailed results. Population, urbanization rate, per capita carbon emissions, energy consumption structure, and trade openness all have positive effects, while per capita GDP has a two-way impact on CO₂ emissions. The influence of urbanization rate and energy consumption structure in the central and western regions increased even faster than in eastern regions, and the impacts of trade openness in lower and higher opening areas are more significant. The population and per capita CO₂ emission have declining influences, among which the influence of population in coastal areas declined more slowly, while the rate of decline of per capita CO₂ emission was positively correlated with the local total CO₂ emissions. The Lorenz curve and the Gini coefficient reveal the inequality distribution of CO₂ emissions in various regions, with the highest CO₂ emissions growth in the medium-economic-level areas, where the key area of carbon mitigation is. Finally, per capita GDP reveals that China as a whole has the trend of inverted N-shape Kuznets curve, and the underdeveloped regions are in the rising stage between the two inflection points, while developed regions are at the end of the rising stage and about to reach the second inflection point.

Economic Structure Transformation and Low-Carbon Development in Energy-Rich Cities: The Case of the Contiguous Area of Shanxi and Shaanxi Provinces, and Inner Mongolia Autonomous Region of China

Energy-rich cities tend to rely on resource-based industries for economic growth, which leads to a great challenge for its low-carbon and sustainable economic development. The contiguous area of Shanxi and Shaanxi Provinces, and the Inner Mongolia Autonomous Region (SSIM) is one of the most important national energy bases in China. Its development pattern, dominated by the coal industry, has led to increasingly prominent structural problems along with difficult low-carbon transition. Taking energy-rich cities in the contiguous area of SSIM as examples, this study analyzes the main drivers of CO2 emissions and explores the role of economic structure transformation in carbon emission reduction during 2002–2012 based on structural decomposition analysis (SDA). The results show that CO2 emissions increase significantly with the coal industry expansion in energy-rich cities. Economic growth and structure are the main drivers of CO2 emission increments. An energy structure dominated by coal and improper product allocation structure can also cause CO2 emission increases. Energy consumption intensity is the main factor curbing CO2 emission growth in energy-rich cities. The decline of agriculture and services contributes to carbon emission reduction, while the expansion of mining and primary energy processing industries has far greater effects on CO2 emission growth. Finally, we propose that energy-rich cities must make more efforts to transform energy-driven economic growth patterns, cultivate new pillar industries by developing high-end manufacturing, improve energy efficiency through more investment in key technologies and the market-oriented reform of energy pricing and develop natural gas and renewable energy to accelerate low-carbon transition.