Spatio-temporal characteristics of the relationship between carbon emissions and economic growth in China's transportation industry

Spatial-temporal characteristics and influencing factors of carbon emission in Chengdu-Chongqing area: an urban transportation perspective

The Chengdu-Chongqing twin-city economic circle is a vital growth pole and a new power source for Chinese high-quality development. Studying the spatial-temporal characteristics of carbon emissions and the role of factors affecting them under the transportation perspective is of great significance for this region to realize the carbon peak and carbon neutrality and to formulate carbon emission reduction policies. We use the exploring spatial data analysis (ESDA) and spatial regression model combined with the STIRPAT model, and research finding: (1) The total carbon emissions in the research area gradually increased from 2014 to 2020, but the growth rate showed a significant decline in 2019. (2) There is significant spatial heterogeneity of carbon emissions in the study area; the hotspot areas of total carbon emissions are in Chongqing and Chengdu, forming a high-low aggregation of carbon emissions. Per capita carbon emissions show a high trend in the southwest and a low in the northeast. (3) From the factors of transportation perspective, highway density and private vehicles have a positive impact on carbon emissions, and urban road areas and public transportation have a very significant inhibition of carbon emissions and a spatial spillover effect. (4) Other factors, such as population size, national economic development, urbanization level, and industrial structure, all have a positive effect on carbon emissions, and disposable income has a negative effect on carbon emissions.

Spatio-temporal evolution and the driving factors of municipal solid waste in Chinese different geographical regions between 2002 and 2020

Urbanization and economic development have contributed to the rapid and massive generation of municipal solid waste (MSW) and significant changes in spatial patterns, which are becoming a serious pollution problem. Previously, macroscopic studies on the driving factors of MSW have been widely conducted at the national level, but the exploration of the driving factors in different geographical regions on a regional scale has not received much attention in the previous literature. This study is based on China, spatial patterns were analyzed using spatial autocorrelation and movement of center of gravity, and time series clustering was used to explore temporal trends. Subsequently, Geodector was adopted to quantify the relationship between MSW generation and driving factors. The results of the study are as follows: 1) By analyzing the spatial pattern of MSW, this study found that MSW showed a spatial pattern of high in the southeast and low in the northwest during 2002-2020, and its separating line was the same as the Hu-line; the average center of gravity of MSW generation in the past 20 years was always located in Henan Province and shifted southward by 339.7 km. 2) The local spatial autocorrelation analysis results showed that the Low-Low clusters moved from southeast to northwest from 2002 to 2020, increasing to 20 cities. High-High clusters mainly appeared in the East Coast and South Coast regions, increasing from 8 to 17 cities in the last 20 years. 3) The analysis of driving factors by Geodetector revealed that Urbanization is the most critical dimension factor influencing MSW generation, with the strongest impact on the East Coast region. The next dimension is Economy, which has the most significant impact on MSW generation in the North West region. Energy is the third dimension that influences MSW generation, with the greatest impact on the North Coast region. The results of this study reveal trends in the spatial and temporal distribution of MSW in different geographic regions of China over the past 20 years and the impact of their driving factors, which can help the Chinese government take action to control MSW in a site-specific manner.

Research on the Carbon Credit Exchange Strategy for Scrap Vehicles Based on Evolutionary Game Theory

In this article, we construct a game model that uses government regulators and scrap vehicle owners as the main parties to investigate the carbon credit exchange strategy of scrap vehicles using evolutionary game theory. The results were validated using Matlab simulation analysis to reveal the dynamic evolution process of the strategy of both sides of the game. A sensitivity analysis of the key parameters was conducted to explore the influence of each parameter on the evolution process and the stabilization trends. The study shows that (1) The time for the game system to reach a steady state is inversely related to the size of the initial willingness of the parties to cooperate. (2) In the mixed steady-state scenario, when the overall return differential between the positive and negative regulatory verification by government departments is positive, the steady state is participation and positive scrapping. (3) When the probability of the government verifying and being successful in verifying the punishment of the owner's negative scrapping behavior increases, both parties of the game will eventually choose the strategy of participation and positive scrapping. When the cost of the government participation strategy and the cost of the government verification strategy increase, both sides of the game will eventually choose the strategy combination of no participation and positive scrapping. (4) When the owner's reward for cooperating with the strategy, the owner's cost of scrapping the vehicle, and the benefits of the owner's negative cooperation strategy change, they will not change the strategy stability results but will affect the time it takes for the game system to reach a stable state. This study has theoretical implications for government policies in the scrapping industry and how to guide vehicle owners to actively scrap their vehicles.

The influencing factors of carbon emissions in the railway transportation industry based on extended LMDI decomposition method: evidence from the BRIC countries

In the twenty-first century, global warming and other environmental issues have become the focus of international attention. The total generation of carbon emissions for the railway transportation industry in the BRIC countries (Brazil, Russia, Indian and China) accounted for 25.73% of the global carbon emissions in this industry during 2017. Therefore, it is necessary to identify the influencing factors of carbon emission in the railway transportation industry for the BRIC, in order to better control and reduce carbon emissions and to achieve the global goal of "net-zero emission." The logarithmic mean divisia index (LMDI) decomposition method was used to examine the factors that influenced carbon emissions from the railway transportation industry in the BRIC from 1997 to 2017. According to the findings, the total carbon emissions of the railway transportation industry in BRIC were 60.92 million tons in 2017, increased by 98.62% compared to 1997. The factor of economic output effect has contributed positively to the increase in carbon emissions in all identified countries. However, the effect of population size effect, energy structure, and transportation intensity effect for carbon emission demonstrated heterogeneity in BRIC. In addition, policy suggestions are put forward for the reduction of carbon emissions from the railway transportation industry in BRIC.

Industrial structure, high-quality development of logistics industry and the economy

The logistics industry is closely related to the high-quality economic development. At different levels of industrial structure, the relationship between high-quality development of the logistics industry and the high-quality economic development will vary, resulting in different roles and paths in promoting economic development. However, there is still a lack of research on the relationship between high-quality development of the logistics industry and high-quality economic development at different levels of industrial structure, and further empirical research is needed. It used the benchmark regression model to analyze the impact of the high-quality development of the logistics industry on high-quality economic development, and the panel threshold model was used to analyze the impact of the logistics industry on high-quality economic development at different levels of industrial structure development. The results show that the high-quality development of the logistics industry has a positive role in promoting the high-quality economic development, and in different levels of industrial structure development, the impact of high-quality development level of logistics industry on the high-quality economic development is different. Therefore, it is necessary to further optimize the industrial structure, promote the deep integration and development of logistics and related industries, and continue to promote the high-quality development of the logistics industry. And when formulating development strategies for the logistics industry, governments and enterprises need to consider factors such as changes in industrial structure, the overall goals of national economy, people's livelihood, and social development, in order to provide solid support for achieving high-quality economic development. This paper demonstrates the importance of high-quality development of the logistics industry in high-quality economic development, and it encourages the adoption of different strategies at different stages of industrial structure development to promote high-quality development of the logistics industry, and achieve high-quality economic development.

Spatial and temporal variations in PM2.5 and associated health risk assessment in Saudi Arabia using remote sensing

Air pollutants, especially ambient particulate matter (PM_2.5), detrimentally impact human health and cause premature deaths. The dynamic characteristics and associated health risks of PM_2.5 are analyzed based on the standard deviational ellipse (SDE) and trend analysis in Saudi Arabia (SAU) from 1998 to 2018 by utilizing recently updated satellite-derived PM_2.5 concentrations (V4.GL.03). The outcomes show that the national average PM_2.5 concentration increased from 28 μg/m³ to 45 μg/m³ with a growth rate of 2.3 μg/m³/year. The center of median PM_2.5 concentrations moved to the southeast over the years studied due to the presence of vast sandy deserts, sand dunes, a busy port, and coastal and industrial areas in this region. The areas of SAU that experienced PM_2.5 concentrations above 35 μg/m³ increased from 20% to 70%. The rapid-fast growth (RFG) class acquired from the unsupervised classification has the fastest growth rate of 2.5 μg/m³/yr, occurring in southeastern SAU, namely Ash-Sharqiyah, Ar-Riyad, and Najran. It covered ∼27% of the total area of SAU over the study period. Whereas, the slow growth (SG) class with a less than 0.2 μg/m³/yr growth rate covered 12% of the total area of SAU, distributed in northwestern regions. The extent of extremely-high risk areas corresponding to greater than 1 × 10³ μg·person/m³ increased from 4% to 11%, particularly in Makkah, Central Al-Madinah, and western Asir, Jizan, mid-eastern Najran, Al-Quassim, and mid-eastern Ar-Riyad and Ash Sharqiyah.

Panel data analysis of energy conservation and emission reduction on high-quality development of logistics industry in Yangtze River Delta of China

With the implementation of Chinese carbon neutrality policy, the Yangtze River Delta calls for great concern. As a benchmark for the development of Chinese logistics industry, it accompanies energy consumption and environmental problems. This study explores how Chinese logistics industry can achieve energy conservation and emission reduction and high-quality development in the context of carbon neutrality. It analyzes the relationship between the logistics industry and economy, energy, as well as environment in Yangtze River Delta. The data is based on China Statistical Yearbook from 2001 to 2019, by means of the entropy method and panel vector autoregressive (PVAR) model. The main findings are summarized as follows: firstly, the economy, industrial structure, energy, and environment have significant impact on the development of logistics industry in Yangtze River Delta. Secondly, the development of logistics industry in Yangtze River Delta is not balanced. The provinces including Jiangsu, Shanghai, Zhejiang, and Anhui have great differences in economy, industrial structure, demographic dividend, energy consumption, and environmental protection, but they show the possibility of complementary advantages. Thirdly, the economic development and energy consumption have bidirectional effects. Environmental protection is relevant to economic development, industrial structure, energy consumption and logistics supply. Finally, some suggestions are provided on how to realize the high-quality development of logistics industry in Yangtze River Delta. In the context of carbon neutrality, it is necessary to consider energy conservation and emission reduction.

The impact of technological innovation on transport carbon emission efficiency in China: Spillover effect or siphon effect?

It is currently unknown whether technological innovation will have spillover or siphon effects on transport carbon emission efficiency (TCEE). Therefore, this paper creates a spatial econometric model to explore the spatial effect of technological innovation on TCEE. Taking 30 provinces in China as examples, we find that the TCEE and the technical innovation index have similar evolution characteristics (numerical value grows, the gap widens), and that both have a spatial distribution that decreases from the eastern coast to the western inland. Further research reveals that TCEE has a considerable siphon effects in China. The siphon effect gets stronger the higher the TCEE. Although technology innovation has the potential to improve TCEE in local province, the siphon effect hinders TCEE improvement in surrounding provinces. Furthermore, heterogeneity research reveals that excessive government intervention will inhibit the promotion of technological innovation on TCEE. Greater levels of government intervention in the middle and western regions than in the eastern region have more obvious inhibitory impacts. The results demonstrate that economic growth and transport structure have played a mediating role in the process of technological innovation promoting TCEE. Regional collaboration and less local protectionism can help the government achieve the dual goals of technological innovation development and TCEE promotion.

Spatiotemporal dynamics and decoupling mechanism of economic growth and carbon emissions in an urban agglomeration of China

Carbon emissions and economic growth are two contradictions in urban development, and their decoupling is related to the sustainable development of cities. This paper took urban agglomeration in the middle reaches of the Yangtze River (UAMRYR), China, as the study area. The Kaya model, the Tapio decoupling model, and the Logarithmic Mean Divisia Index (LMDI) model were adopted to analyze the spatiotemporal differentiation of carbon emissions, the decoupling of economic activities, and driving factors. The results indicate that (1) carbon emissions increased by 66% in the study period, but the growth momentum was curbed after 2015. Low level and medium level areas continue to decrease, and relatively high level area gradually become dominant. (2) Spatially, carbon emissions are in a pattern of middle-hot and east-cold. Jiangxi is in the sub-cold and coldspot area, while the hotspot area is driven by the transformation from Wuhan's single-core to Wuhan and Changsha's dual-core. (3) Since 2010, most cities have been in a good decoupling state, and weak decoupling cities have risen from 35.5% in the initial period to 87.1% in 2010-2011, but the decoupling situation of industrial cities with more high-energy-consuming industries still rebounded slightly. (4) The economic level and energy intensity effect had the most significant impact on the economic decoupling of carbon emissions, whose absolute contribution rates were greater than 35%. Urbanization and economic level both play a positive role in promoting carbon emissions, and the energy intensity plays a negative role in retarding carbon emissions. The population effect was mainly manifested in carbon increase from 2006 to 2011, and 45.2% of the cities from 2011 to 2017 turned into carbon suppression. Finally, we suggest that decoupling carbon emissions from economic growth requires developing green urbanization and a decarbonized economy, optimizing the structure of energy consumption and guiding rational population flow.

Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces

In the context of green and high-quality development, effectively enhancing industrial carbon emission efficiency is critical for reducing carbon emissions and achieving sustainable economic growth. This study explored this research area using three models: the super-efficient SBM model was used to measure the industrial carbon emission efficiency of 48 cities in the Pearl River Basin from 2009 to 2017; the exploratory spatiotemporal data analysis method was used to reveal the spatiotemporal interaction characteristics of industrial carbon emission efficiency; and the geographical detectors and geographically weighted regression model were employed to explore the influencing factors. The results are as follows: (1) The Pearl River Basin’s industrial carbon emission efficiency steadily increased from 2009 to 2017, with an average annual growth rate of 0.18 percent, but the industrial carbon emission efficiency of some sites remains low; (2) The local spatiotemporal pattern of industrial carbon emission efficiency is solitary and spatially dependent; (3) The spatial variation of industrial carbon emission efficiency is influenced by a number of factors, including the industrialization level, openness to the outside world, the science and technology level, energy consumption intensity, and productivity level, with the productivity level, industrialization level, and openness to the outside world being the most important. Among these factors, the productivity level, science and technology level, openness to the outside world, and industrialization level all have a positive correlation with industrial carbon emission efficiency, but energy consumption intensity has a negative correlation. This study provides an integrated framework using exploratory spatiotemporal analysis and geographically weighted regression to examine carbon emission efficiency among cities. It can serve as a technical support for carbon reduction policies in cities within the Pearl River Basin, as well as a reference for industrial carbon emission studies of other regions of the world.

Characteristics, decoupling effect, and driving factors of regional tourism's carbon emissions in China

By revealing the temporal and spatial differentiation of China's regional tourism carbon emissions and its decoupling relationship with tourism economic growth and identifying the key factors affecting tourism carbon emissions, this paper is expected to provide a reference for the formulation and implementation of China's regional tourism industry emission reduction policies and measures. Using the tourism's carbon emission data of 30 provinces (cities) in China from 2007 to 2019, we have established a logarithmic mean Divisia index (LMDI) model to identify the main driving factors of carbon emissions related to tourism and a Tapio decoupling model to analyze the decoupling relationship between tourism's carbon emissions and tourism-driven economic growth. Our analysis suggests that China's regional tourism's carbon emissions are growing significantly with marked differences across its regions. Although there are observed fluctuations in the decoupling relationship between regional tourism's carbon emissions and tourism-driven economic growth in China, the data exhibit a primary characteristic of weak decoupling. Nonetheless, the degree of decoupling is rising to various extents across regions. Three of the five driving factors investigated are also found to affect emissions. Both tourism scale and tourism consumption lead to the growth of tourism's carbon emissions, while energy intensity has a significant effect on reducing emissions. These effects differ across regions.

Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China

The rapid development of public buildings has greatly increased the country’s energy consumption and carbon emissions. Excessive carbon emissions contribute to global warming. This paper aims to measure the carbon emissions in the operation of public buildings, and to identify the multiple influencing factors of carbon emissions in operational public buildings. First, the spatial and temporal variation characteristics of carbon emissions from public buildings in 30 provinces of China from 2008–2019 are analyzed. Second, a green building index is constructed, and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is utilized to explore the relationship between each influencing factor and carbon emissions, using spatial and temporal geographically weighted regression analysis. The results show that the effects of population, urbanization rate, GDP per capita, green building index, and industrial structure on carbon emissions from public buildings all show spatial correlation and differences. There are east-west differences in the operational carbon emissions of public buildings in China’s provinces. Cluster evolution shows a spatially increasing trend from west to east. To some extent, policymakers can develop appropriate policies for different provinces through the findings.

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.

The Impact of Diabetes on Vascular Disease: Progress from the Perspective of Epidemics and Treatments

At present, the global incidence of diabetes has increased in countries with large populations, and the changes in developing regions are particularly worthy of attention. In the past 40 years or so, the income situation in China, India, and other countries has exploded, leading to changes in the way of life and work as well as an increase in the prevalence of diabetes. Metabolic disorders caused by diabetes can lead to secondary vascular complications, which have long-term malignant effects on the heart, kidneys, brain, and other vital organs of patients. Adequate primary prevention measures are needed to reduce the incidence of diabetic vascular complications, and more attention should be given to treatment after the disease. To this end, it is necessary to determine a standardized drug and physical therapy system and to build a more efficient and low-cost chronic disease management system.

Analysis of the spatial relevance and influencing factors of carbon emissions in the logistics industry from China

This study attempts to analyze the impact of population, property, technology, energy factors, and spatial agglomeration in the logistics industry on carbon emissions. To achieve the goal of peak carbon and carbon neutrality, the relationship between influencing factors and carbon emissions was analyzed based on panel data from the logistics industry for 30 provinces in China from 2003 to 2017 using an improved STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model and a spatial lag model (SLM). The results show that population, property, technology, and energy factors in the logistics industry all have different degrees of influence on carbon emissions, wherein population, energy, and property have a greater influence, which implies that carbon emission reduction policies can be carried out considering the relevant aspects. In addition, under the influence of spatial agglomeration, the degree of influence of freight mileage (FM), total fixed-asset investment (TFAI), and industry population (IPOP) on carbon emissions decreases, and the degree of influence of energy intensity (EI) and industry per capita GDP (IPCG) increases. This suggests that corresponding emission reduction policies should be formulated for large urban areas based on technological innovation, infrastructure, and talent training, while smaller urban areas can focus on developing new energy and industrial economies. These findings help to complement the existing literature and provide policymakers with some insights related to urban logistics development.

Regional inequality of total factor CO2 emission performance and its geographical detection in the China's transportation industry

The total factor CO₂ emission performance (TFCEP) of transportation industry has received increasing research interests, while existing literature pays little attention to its regional inequality and driving factors. In order to uncover the regional inequality of TFCEP in China's transportation industry, this paper used Theil index and combined with geographical detector model (GDM) to explore the driving factors and their interactions on TFCEP in Chinese transportation industry. The results revealed that the TFCEP of transportation industry showed a promising increase during 2003-2017 with an annual growth rate of 0.12%, and the improvement was contributed by the technical efficiency change. The TFCEP in the Eastern region performed better than that in the Northeast, Central, and Western region. Regional inequality of TFCEP did exist and exhibited an obvious downward trend. The within-region inequality had a greater impact on the inequalities than between region. Freight turnover was the main driving factor of TFCEP in the transportation industry, followed by the energy intensity and per-capita GDP. In the Eastern and Western regions, freight turnover had the greatest impact on TFCEP, while in the Central and Northeastern regions, urbanization rate and energy intensity were the dominant factors, respectively. The interactions between energy intensity and freight turnover were highly influential. This paper provides important insights for different regions to formulate targeted carbon emission reduction policies.

Spatial-temporal distribution and evolutionary characteristics of water environment sudden pollution incidents in China from 2006 to 2018

Sudden water pollution incidents (SWPI) are random and harmful, which is a problem that cannot be ignored in ecological environment governance and economic development. Identifying spatial-temporal distribution characteristics of SWPI is essential for the disaster prevention and the early warning of water environment. The Kernel Density (DE) and spatial mean center of SWPI transfer curve were used to explore the characteristics with the dataset of 1174 cases from 2006 to 2018 in China. Results showed that: (1) From the time point of view, there was an overall upward trend in the overall number of SWPI. (2) At a regional scale, Eastern China, Southwest China, and Southern China underwent a high frequency with 69.93% of SWPI. The Fujian, Guangdong and Chongqing provinces were specified as the top 3 provinces with incident frequencies. The Yangtze River Basin and the Pearl River Basin were two regions where water pollution incidents occur more often, more than 50% of incidents among basins in China every year. (3) In general, SWPI presents a northeast-southwest distribution pattern and center of SWPI moves in the direction of west by south. (4) More than half of the incidents (57.24% of the total) were induced by illegal pollutant discharge and production safety accidents.

Semen quality as a potential susceptibility indicator to SARS-CoV-2 insults in polluted areas

The epidemic of the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has impacted worldwide with its infectious spread and mortality rate. Thousands of articles have been published to tackle this crisis and many of these have indicated that high air pollution levels may be a contributing factor to high outbreak rates of COVID-19. Atmospheric pollutants, indeed, producing oxidative stress, inflammation, immuno-unbalance, and systemic coagulation, may be a possible significant co-factor of further damage, rendering the body prone to infections by a variety of pathogens, including viruses. Spermatozoa are extremely responsive to prooxidative effects produced by environmental pollutants and may serve as a powerful alert that signals the extent that environmental pressure, in a specific area, is doing damage to humans. In order to improve our current knowledge on this topic, this review article summarizes the relevant current observations emphasizing the weight that environmental pollution has on the sensitivity of a given population to several diseases and how semen quality, may be a potential indicator of sensitivity for virus insults (including SARS-CoV-2) in high polluted areas, and help to predict the risk for harmful effects of the SARS-CoV-2 epidemic. In addition, this review focused on the potential routes of virus transmission that may represent a population health risk and also identified the areas of critical importance that require urgent research to assess and manage the COVID-19 outbreak.