An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China

Due to the increasing and rapid economic and population growth over the past years, urban areas have become the focus of carbon-emission-reduction policies. Meanwhile, they are also continuously subjected to environmental pressure from urban development. Therefore, this study used the STIRPAT model to develop an index to capture the environmental pressure from urban development based on a panel dataset of 284 prefecture cities from 2006 to 2022 in mainland China. Subsequently, a fixed effect model and a quantile regression model were used to test the impact of the environmental pressure from urban development on the level of urban carbon emissions. The results showed a notable relationship between the environmental pressure from urban development and the level of urban carbon emissions. More specifically, for every one-unit increase in the index, the urban carbon emission level increased by 1.4%. This result was found stable after a series of robustness tests. Furthermore, quantile regression showed that before the 50th percentile, the environmental pressure from urban development had a positive impact on the level of urban carbon emissions, whereas after the 50th percentile, it had a negative impact. Finally, heterogeneity analysis indicated that, compared to in large cities, the positive impact of the environmental pressure from urban development on urban carbon emission levels was more significant in small-sized, medium-sized, and megacities. Therefore, it is recommended to continuously optimize the context for business and trading and promote the industrial structure. Carbon emission reduction targets should be differentiated based on development levels of cities. While implementing carbon emission reduction measures in urban areas, population and other conditions should be taken into account.

Global warming caused by excessive emissions of CO2 and other greenhouse gases is one of the greatest challenges for mankind in the 21st century. China is the world’s largest carbon emitter and its transportation industry is one of the fastest growing sectors for carbon emissions. However, China is a vast country with different levels of carbon emissions in the transportation industry. Therefore, it is helpful for the Chinese government to formulate a reasonable policy of regional carbon emissions control by studying the factors influencing the carbon emissions of the Chinese transportation industry at the regional level. Based on data from 1997 to 2017, this paper adopts the logarithmic mean divisia index (LMDI) decomposition method to analyze the influencing degree of several major factors on the carbon emissions of transportation industry in different regions, puts forward some suggestions according to local conditions, and provides references for the carbon reduction of Chinese transportation industry. The results show that (1) in 2017, the total carbon emissions of the Chinese transportation industry were 714.58 million tons, being 5.59 times of those in 1997, with an average annual growth rate of 9.89%. Among them, the carbon emissions on the Eastern Coast were rising linearly and higher than those in other regions. The carbon emissions in the Great Northwest were always lower than those in other regions, with only 38.75 million tons in 2017. (2) Economic output effect is the most important factor to promote the carbon emissions of transportation industry in various regions. Among them, the contribution values of economic output effect to carbon emissions on the Eastern Coast, the Southern Coast and the Great Northwest continued to rise, while the contribution values of economic output effect to carbon emissions in the other five regions decreased in the fourth stage. (3) The population size effect promoted the carbon emissions of the transportation industry in various regions, but the population size effect of the Northeast had a significant inhibitory influence on the carbon emissions in the fourth stage. (4) The regional energy intensity effect in most stages inhibited carbon emissions of the transportation industry. Among them, the energy intensity effects of the North Coast and the Southern Coast in the two stages had obvious inhibitory influences on carbon emissions of the transportation industry, but the contribution values of the energy intensity effect in the Great Northwest and the Northeast were positive in the fourth stage. (5) Except for the Great Southwest, the industry-scale effects of other regions had inhibited the carbon emissions of transportation industry in all regions. (6) The influences of the carbon emissions coefficient effect on carbon emissions in different regions were not significant and their inhibitory effects were relatively small.

As one of China s fastest growing regions, Beijing Tianjin Hebei (B-T-H), which contains one of the most intensive logistics networks in China, developed rapidly from 2005 to 2016. However, such growth also led to increases in energy consumption and carbon emissions. From 2005 to 2011, the growth rate of logistics carbon emissions in the B-T-H was maintained at 8% 10%. Subsequently, owing to policies and technological progress related to energy saving and emission reduction, a positive decrease in carbon emissions was observed. The statistical research revealed that the absolute difference in the logistics carbon emission level in B-T-H fluctuated greatly, the level of logistics carbon emissions was unbalanced. Use of the logarithmic mean Divisia index decomposition method revealed that the industrial scale and population scale were promoting factors of logistics carbon emissions in B-T-H, whereas energy intensity was the main inhibiting factor. The effect of the energy structure was not significant. Differences in the influencing factors of the three B-T-H areas were observed. Some suggestions are proposed: The population of B-T-H should be strictly controlled, energy intensity should be reduced, the energy structure and traffic structure should be adjusted, and a cooperation mechanism should be established.

Influencing factors of carbon emissions in transportation industry based on C[sbnd]D function and LMDI decomposition model: China as an example

The streetscape of any city reflects the natural built fabric of the street and adds to the experiential quality of that space. A sustainable streetscape ensures that the spaces are long-lasting and functional as a part of the greater sustainable eco-system. Over the past two centuries, mankind has increased the concentration of carbon dioxide (CO2) in the atmosphere from 280 to more than 380 parts per million by volume, and it is still increasing every day. If Earth continues to emit carbon without control, the surface temperature is expected to rise by 3.4 °C by the end of this century. Climate change of that magnitude would likely have serious, long-lasting, and, in many cases, devastating consequences for the planet Earth. Egypt has become one of the biggest emitters of atmospheric pollutants from the transportation sector. The level of carbon emissions and its effect on air quality are placed high on the research agenda. The transportation sector has a great impact on increasing CO2 emissions. The transportation and traffic sectors produce a quarter of the global CO2 emissions due to the heavy use of fossil fuels. This research aims to study the effects of street shape and design on CO2 production. This paper presents an analysis of road transportation in Egypt, taking Helmiet EL-Zaitoun as the main case study, with a focus on energy demand and carbon dioxide (CO2) emissions. Carbon emissions are measured using “Testo 315-3” as a measuring instrument to identify the relationship between street design and quality on the amount of carbon emissions produced. The results of this research suggest that street quality affects the amount of CO2 emissions produced. A car moving at a constant speed will produce fewer CO2 emissions than a car forced to start–stop every while because of external factors such as cracks in the roads occurring from bad streetscape elements. The presented research provides a set of guidelines to enhance the quality of the streetscape and design in order to reduce the amount of CO2 produced in the streets.

How globalization is reshaping the environmental quality in G7 economies in the presence of renewable energy initiatives?

Factors that influence carbon emissions due to energy consumption based on different stages and sectors in China

Summary Cities, contributing more than 75% of global carbon emissions, are at the heart of climate change mitigation. Given cities' heterogeneity, they need specific low-carbon roadmaps instead of one-size-fits-all approaches. Here, we present the most detailed and up-to-date accounts of CO2 emissions for 294 cities in China and examine the extent to which their economic growth was decoupled from emissions. Results show that from 2005 to 2015, only 11% of cities exhibited strong decoupling, whereas 65.6% showed weak decoupling, and 23.4% showed no decoupling. We attribute the economic-emission decoupling in cities to several socioeconomic factors (i.e., structure and size of the economy, emission intensity, and population size) and find that the decline in emission intensity via improvement in production and carbon efficiency (e.g., decarbonizing the energy mix via building a renewable energy system) is the most important one. The experience and status quo of carbon emissions and emission-GDP (gross domestic product) decoupling in Chinese cities may have implications for other developing economies to design low-carbon development pathways.

Much research has been done on identifying socio-economic household links in developed countries. However, the study of household carbon emission (HCE) levels and related variables still needs to be examined, especially in developing countries. The study uses an ordinary least squares model to pinpoint the socio-economic elements that affect a household's carbon emission levels. SUSENAS (National Socio-economic Survey) data from March 2019 and 2021, covering 655,694 households, were used. This study used ordinary least squares (OLS) for the regression and dominance analyses (DA) to determine the most crucial factors affecting the HCE. The household characteristics, individuals, and residential conditions are used to measure socio-economic situations. The DA analysis shows that income and household size are the most crucial determinants of HCE. The OLS analysis reveals that the income variable exhibits a non-linear relationship with HCE as an inverted U-shape in the total HCE and most consumption categories. Wealthier households generate higher levels of household carbon emissions than poorer households. The variable of household size demonstrates a positive relationship with the HCE. The composition of household members also significantly affects household carbon emission levels, where the presence of working members and toddlers tends to increase household carbon emissions. The research also finds differences in consumption patterns between urban and rural households, resulting in varying levels of carbon emissions. The findings of this study can assist policymakers in formulating targeted policies to reduce household carbon emissions.

Carbon emissions have become a global issue of increasing concern due to their detrimental impact on the environment. Efforts to combat rising emissions have been taken globally. Despite China’s commitment to globalization, policymakers have faced challenges in adequately addressing this pressing issue. This paper aims to fill this gap by exploring a specific aspect of international trade, namely service trade. We theoretically analyzed the impact of service trade on carbon emissions and then empirically examined the impact using panel data from 2009 to 2019 of 30 provincial regions in China. Specifically, a non-linear model was used to capture the direct effect, particularly the potential non-linear relationship; a mediating effect model was applied to investigate the indirect effects; and a panel quantile model was adopted to examine the heterogeneity of the impact across different levels of carbon emissions. The research revealed: (1) The impact of service trade on carbon emissions exhibits a non-linear characteristic with a significant inverted U-shaped relationship being evident, indicating that the development of service trade can ultimately contribute to carbon reduction; (2) service trade can directly impact carbon emissions through its scale effect, while mechanism analysis showed that service trade can indirectly affect carbon emissions through its technological and structural effects, with carbon emission reduction also relying on these mechanisms; (3) There is significant heterogeneity in the impact of service trade on carbon emissions across geographic regions and at different levels of carbon emissions in China.

Economic growth in the Beijing–Tianjin–Hebei region has been achieved by consuming large amounts of fossil fuels. This produces a large number of pollutants, which damage the physical and mental health of residents, and prevent sustainable economic development. The most urgent task at present is improving the quality of the environment. This paper takes carbon emission as a pollution index, and adopts an extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model in order to study the impact of the optimization of industry structure (in particular the reduction of the proportion of energy-intensive secondary industry), the optimization of the energy structure, and technological improvements on the atmospheric environmental quality. We obtain some important and enlightening discoveries. First of all, the rapid economic growth that has been based on magnanimous fossil fuel consumption is still the main reason for the deterioration of the atmospheric environment. This means that the main driving force of economic growth still comes from high pollution industries, despite a strategy for the transformation of the pattern of economic growth having been proposed for many years. Second, the optimization of the industrial structure has not played a significant role in promoting the reduction of carbon emissions. Through further research, we believe that this may be due to the low-quality development of the third industry. In other words, the traditional service industry related to high energy consumption accounts for a large proportion in regional total output, while the high-end service industry related to small pollution accounts for a relatively small proportion. Third, reducing the consumption of coal and improving the technological level can effectively curb the deterioration of the environmental quality. In addition, we find that transboundary pollution is an important factor affecting the environment in this region, and the earnings of any unilateral treatment action is small. As a result, joint pollution control under the supervision of the central government can produce greater benefits. Therefore, we believe that the transition of the economic growth pattern, and the optimization of the energy and industry structures (especially developing the high-end service industry) are effective ways to improve the environmental quality.

Based on provincial panel data from 2008 to 2017 as the research sample in China, this study examines the impact of financial development on carbon emissions. Regression models are employed to estimate the influence of financial development on carbon emissions in different regions. The research reveals that the level of carbon emissions is influenced by differentiation and heterogeneity, with different regions showing varying sensitivities to factors such as financial development, technological innovation, and industrial structure. Financial efficiency has a negative impact on carbon emissions, being positive in regions with low carbon emissions. In areas with higher carbon emissions, the impact of financial efficiency on carbon emissions is greater. Industrial structure has a negative impact on carbon emissions, and in regions with lower carbon emissions, the upgrading of industrial structure has a greater impact on carbon reduction. Technological innovation has a negative impact on carbon emissions in regions with medium to high levels of emissions, while its impact in low-emission areas is positive but not significant. Per capita GDP, energy structure, and urbanization level overall have a positive impact, while foreign investment has a negative impact. The introduction of technological innovation as a moderating variable significantly enhances the significance level of financial efficiency.

The effect of energy resources on economic growth and carbon emissions: A way forward to carbon neutrality in an emerging economy

In order to better understand the digital economy on carbon emissions, the intermediary effect model based on technological innovation and the panel threshold model based on trade openness and energy intensity are used to study the relationship between the digital economy and carbon emissions in G7 countries. In addition, a quantile regression analysis on the impact of the digital economy under different carbon emission levels is also conducted. The empirical results show that: (i) the digital economy has a significant inhibitory effect on per capita carbon emissions. (ii) The mechanism analysis shows that the digital economy can indirectly affect per capita carbon emissions by affecting technological innovation. (iii) The threshold effect of trade openness and energy intensity is significant. Under high openness and low energy intensity, the digital economy has a stronger inhibitory effect on per capita carbon emissions. (iv) The quantile regression results show that the carbon emission reduction effect of the digital economy is greater at the low-carbon emission level.

As the world’s largest producer, consumer, and exporter of textiles and apparel, China plays a pivotal role in the carbon emission reduction of the industry. This paper employs the “Tapio” decoupling model to analyze the decoupling relationship between carbon emissions and economic growth in China’s textile industry across 30 provinces from 2001 to 2020. The LMDI model is then used to decompose the influencing factors of carbon emissions, followed by a comparative study of four distinct periods and three regions. The findings reveal that: (1) From 2001 to 2020, both carbon emissions and GDP growth in China’s textile industry exhibited a “rise then decline” trend. Carbon emissions primarily stemmed from coal consumption, followed by gasoline and natural gas. The energy structure had been gradually optimized, with the shares of coal and gasoline in total consumption declining, while the share of natural gas has increased. (2) Carbon emissions in China’s textile industry had achieved strong decoupling from economic growth over the 20-year study period. The decoupling status at different periods was as follows: weak decoupling (2001–2005), weak decoupling (2006–2010), strong decoupling (2011–2015), and weak negative decoupling (2016–2020). Comparing different regions, Central China exhibited strong decoupling, while East China and West China both experienced weak decoupling. (3) Per capita output value was the main driving factor behind the growth of carbon emissions, while energy consumption intensity factor played the most significant role in inhibiting emissions. Additionally, employment scale factor promoted carbon emissions in the first two periods but significantly suppressed them in the last two periods. The energy structure factor gradually strengthened its suppressive effect on carbon emissions, although its overall impact remained limited. Finally, based on these findings, feasible and practical policy recommendations are proposed.