The differences that methods make: Cross-border power flows and accounting for carbon emissions from electricity use

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.

The transport sector is the major green-house gas emitter and most rapidly growing sector in terms of consuming energy in China. Understanding the driving forces behind carbon emission is a prerequisite for reducing carbon emissions and finding a balance between economic growth and carbon emissions. The purpose of this paper is to identify the impact of the factors which influence the level of carbon emissions from the transportation sector in the Beijing-Tianjin-Hebei (BTH) area, China, using decomposition model, combined with a decoupling elasticity index. The results of our study indicate that: (1) changes in the level of carbon emissions from the transportation sector are not always synchronized with changes in economic growth. (2) The decoupling state between the carbon emissions and economic growth of Tianjin and Beijing can be roughly divided into two phases. The first phase was during the 2005 to 2009 period, when the decoupling state was pessimistic. The second phase was from 2009 to 2013, when the decoupling state became better overall and was mainly dominated by weak decoupling. Conversely, the decoupling state of Hebei was mainly weak during this period. (3) Economic growth and population size play positive roles in increasing the levels of transportation-related carbon emissions in BTH. However, the energy structure is a negative force. The effect of energy intensity always plays a negative role in Tianjin and Hebei, but positive in Beijing. The industrial structure effect shows a fluctuating trend, but the cumulative effect value is negative, and negative interaction is prominent. Finally, this paper gives some suggestions on how to develop low-carbon transport in BTH area.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

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

Global warming is one of the most serious environmental problems that the world faces today. Millions of human lives are at risk, hence the subject has gained enormous attention within academia and the research arena. Literature shows that the primary cause of global climate change or global warming is carbon (CO2) emissions. In the literature, a number of studies have investigated the factors affecting the overall level of carbon emission. However, in recent years, consumption-based carbon emissions have occupied the center stage in research related to international trade and environmental concerns. The recently emerged idea of carbon emissions based on consumption differs from conventional accounting (i.e., carbon emissions based on production) in that it highlights the importance of international trade in national carbon emissions. Unlike the previous studies that examined the symmetric effect of international trade on consumption-based carbon emission, the present study examines the asymmetric effect of international trade on consumption-based carbon emissions of emerging economies. To get empirical estimates, the study applies a Nonlinear Panel Autoregressive Distributive Lag (NPARDL) approach. The estimates show that both in the short and long run, a positive shock in exports significantly reduces consumption-based carbon emissions in developing economies. Whereas, a negative shock in exports has an insignificant impact on carbon emissions. For imports, the results show that, over time, positive shocks in imports significantly increase consumption-based carbon emissions, while the impact of negative shocks is insignificant. Finally, it is recommended for the policymakers to target the export industries for relevant policy interventions, which are less polluting but crucial for economic growth.

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.

<p>Elucidating the mechanism of the correlation between urban spatial agglomeration processes and carbon emission levels can provide valuable scientific underpinnings for town planning under the constraints of low-carbon goals. This study investigates the characteristics of urban spatial agglomeration in Jilin Province and its association with carbon emission levels. This is achieved by constructing spatial agglomeration indicators, and multiple linear regression models. The findings are as follows: (1) Over the period from 2004 to 2020, the overall trend of urban spatial agglomeration in Jilin Province was characterized by an initial decline, followed by a subsequent increase, but ultimately rising as a whole. (2) A significant negative correlation exists between urban spatial agglomeration and carbon emission levels. Spatial agglomeration in towns exerted a strong influence on changes in regional carbon emission levels.. Specifically, spatial clustering in service industries demonstrated a more pronounced impact on carbon emissions than was the case in production sectors. (3) A regression analysis reveals that the spatial agglomeration of urban population and economic factors has a significant negative impact on both total carbon emissions and carbon emission intensity. The spatial concentration of urban populations is found to have a marked influence in enhancing carbon emission efficiency.</p>

Previous research has investigated the connections between foreign direct investment (FDI), carbon emissions (CO2), and foreign trade openness. However, these past studies did not specifically focus on industrial sectors in China and their carbon emissions, thus leaving a gap in understanding this relationship. In our study, we aim to contribute to the existing body of knowledge by employing a threshold regression model with a threshold variable. This model calculates how strongly carbon emissions are produced to assess the impact of the industrial sector on carbon emissions. Our findings reveal that foreign trade openness and FDI have a threefold threshold impact on industrial carbon emissions. The effect of FDI on carbon emissions in the industrial sector shows a pattern of initially lowering and then increasing the emissions, indicating potential harm. Conversely, the impact of foreign trade openness on carbon emissions exhibits both positive and negative effects. While foreign trade openness exacerbates carbon emissions in economic sectors with lower carbon intensity, it helps mitigate emissions in sectors with high- carbon emission levels. Furthermore, our study identifies that the intensity of economic activity, per capita GDP, and the number of employees all significantly influence the industrial sector’s carbon emissions. By employing the latest cutting-edge methodology, our research opens the door for extrapolating these findings to other nations for a comprehensive analysis.

Land-use change significantly contributes to carbon emission. Analyzing this relationship fosters exploration of low-carbon, efficient land-use patterns at regional levels. Using ArcGIS 10.5 and the PLUS model, this study investigated land transfer trends across six counties and one district in the Malian River Basin between 2000 and 2020. It quantified carbon emissions from land use and performed spatial distribution analysis using land-use and socio-economic data. The study demonstrates the following: (1) Between 2010 and 2020, significant land-use changes occurred in the Malian River Basin with 72,919.49 km2 of land undergoing transformation. Notably, the farmland-to-forest and grassland conversion project in Qingyang City was a major factor contributing to the shift from arable land to forest and grassland. (2) Natural factors influencing land conversion in the Loess Plateau region primarily include precipitation and elevation. Conversely, social factors such as population density, road networks, and local government establishments in districts and counties are pivotal in driving land-use changes within the Malian River Basin. (3) Carbon emissions vary significantly among different land-use types, with building land, cropland, unutilized land, watershed, grassland, and forest land showing descending emissions. The rapid expansion of building land notably increases carbon emissions in the study region, while forest land, a significant carbon sink, absorbs approximately 88% of total carbon emissions. (4) Districts and counties in the study area exhibit varying levels of carbon emissions, with Ning County, Xifeng District, Huan County, Qingcheng County, Zhengning County, Heshui County, and Huachi County listed in descending order. Regions with higher carbon emissions typically host abundant energy resources and significant energy production and consumption activities. Variations in carbon emission levels are largely influenced by resource availability and development priorities. Variations in resource levels and developmental focus are pivotal in explaining differences in carbon emission levels. Thus, it is crucial to explore the dynamic interplay between land-use carbon emission efficiency and land evolution in the Malian River Basin. This research will support ecological management and sustainable economic development in the Yellow River Basin, while also contributing to the achievement of the “double carbon” goals.

It is of great significance to formulate differentiated carbon emission reduction policies to clarify spatio-temporal characteristics and driving factors of carbon emissions in different cities and cities at different scales. By fitting nighttime light data (NTL) of long time series from 2000 to 2020, a carbon emission estimation model of Pearl River Delta urban agglomeration at city, county, and grid unit levels was built to quickly and accurately estimate carbon emission in the Delta cities above county level. Combining spatial statistics, spatial autocorrelation, Emerging Spatio-Temporal Hotspot Analysis (ES-THA), and Theil index (TL), this study explored the spatio-temporal differentiation of urban carbon emissions in the Delta and used a geographical detector to determine the influencing factors of the differentiation. The results of the study showed that NTL could replace a statistical yearbook in calculating carbon emissions of cities at or above county level. The calculation error was less than 18.7385% in the Delta. The three levels of carbon emissions in the Delta increased in a fluctuating manner, and the spatial distribution difference in carbon emissions at the municipal and county levels was small. Therefore, a combination of municipal and county scales can be implemented to achieve precise emission reduction at both macro and micro levels. The central and eastern parts of the agglomeration, including Guangzhou (Gz), Shenzhen (Sz), Zhongshan (Zs), and Huizhou (Hz), were a high-value clustering and spatio-temporal hot spots of carbon emissions. Zhaoqing (Zq) in the northwestern part of the agglomeration has always been a low-value clustering and a spatio-temporal cold spot because of its population, economy, and geographical location. The carbon emission differences in the Delta cities were mainly caused by carbon emission differences within the cities at the municipal level, and the cities faced the challenge of regional differences in the reduction in per capita carbon emissions. As the most influential single factor, spatial interaction between economic development and various factors was the main driving force for the growth of carbon emissions. Therefore, the results of this study provide a scientific theory and information support for carbon emission estimation and prediction, differentiated emission reduction measures, and carbon neutrality of cities in the Delta.

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.

Although the Chinese government emphasizes the significance of public transportation development and encourages green travel, no empirical study has examined whether the expansion of public transportation facilitates the mitigation of carbon emissions. To this end, we employ a panel quantile regression to test the endogenous relationship between public transportation scale and carbon emissions. The results suggest that the effect of public transportation scale on carbon emissions is heterogeneous across China's provinces based on the level of carbon emissions. Even so, the results still support a stable inverted U-shaped relationship between public transportation scale and carbon emissions for provinces with different levels of carbon emissions. That is, when public transportation scale exceeds a threshold value, the relationship between public transportation and carbon emissions will turn from positive to negative. Our findings provide evidence advocating for public transportation development and green travel. It is of great significance for China to respond to climate changes.

Using hand-collected carbon emissions data for 2006-2008 that S&P 500 firms disclosed voluntarily to the Carbon Disclosure Project, we investigate the relationship between carbon emission levels and firm value. The study is motivated by a relationship between carbon emissions and global climate change that some informed observers expect will drive a redistribution of value from firms that do not control carbon emissions successfully to firms that do (GS Sustain [2009]). Concern about carbon emissions is shared by corporate executives, boards of directors, investors, creditors, standard setters, government regulators, and NGOs. We control for systematic firm-level characteristics that may be associated with managers’ decisions whether or not to voluntarily disclose carbon emissions. We find a negative association between carbon emission levels and firm value, contingent upon firms voluntarily disclosing their carbon emissions in the first place. Our sensitivity analyses and robustness test results are qualitatively similar to our main results.

The digital economy (DE) not only drives economic innovation and development but also has significant environmental effects by promoting lower carbon emissions. To investigate the spatial effects of DE on urban carbon emissions, this study comprehensively measures the level of DE development based on the panel data from 11 typical cities in Zhejiang Province from 2011 to 2020, by comparing analysis using different regression models. The following conclusions are obtained: (1) The total carbon emissions (TC) of Zhejiang cities in general show a fluctuating change trend of first increasing and then slowly decreasing, while carbon emission intensity and carbon emission per capita in general show a fluctuating change trend of decreasing. Cities with high TC are primarily concentrated in the Hangzhou Bay city cluster, accounted for 62 ~ 65% of the province's carbon emissions. The development of the DE in Zhejiang cities shows steady growth, but there are large differences among cities, with Hangzhou and Ningbo standing out as particularly prominent. (2) There is a significant inverted U-shaped relationship between the DE and the level of carbon emissions in Zhejiang Province. The influence coefficient of the DE on the primary term of TC is 0.613, and the influence coefficient of the quadratic term of TC is - 1.008. (3) In terms of the spatial spillover effect of the DE on carbon emissions, the study finds that compared to the direct effect, the spatial spillover effect is not significant. However, the allocation of transport resources shows a positive spatial spillover effect (increasing carbon emissions, coefficient value is 0.138), while technological progress shows a somewhat negative spatial spillover effect (decreasing carbon emissions, coefficient value is - 0.035). (4) The study also finds that the smart city pilot policy significantly reduces urban carbon emissions. Moreover, the effect of the DE on carbon emissions is confirmed through the significance test of the quadratic term when replacing the geographical and economic distance weight matrices. This indicates that the empirical findings are robust to these tests. Finally, several countermeasures to reduce carbon emissions are proposed from the perspective of DE development.

The effect of working environment-ill health aspects on the carbon emission level of a manufacturing system