Drivers Of Carbon Emissions Research Articles

Interactive geographical and temporal weighted regression to explore spatio-temporal characteristics and drivers of carbon emissions

Countries need a science-informed strategy to manage carbon peaking and carbon neutrality. This study extends the geographically and temporally weighted regression (GTWR) model to include the GeoDetector's factor interaction detection plate to investigate the spatio-temporal characteristics of the factors influencing regional carbon emissions in the Yangtze River Economic Belt (YEB), an important economic area in China. The results from the proposed interactive geographically and temporally weighted regression (IGTWR) model indicate that the evolution of carbon emissions can be categorized into two phases in the temporal dimension. In terms of spatial distribution, the carbon emissions of the YEB are distributed in a northeastsouthwest direction, are centered in Hubei Province and cover a broad geographical range. Both the drivers of carbon emissions and their factor interactions possess spatial heterogeneity.

The Nexus between Financial Development and Carbon Emission in OIC Countries

Economic activities are important factors for the development of countries. However, many countries give too much importance to the economy without paying attention to the country's environment as a result of ongoing economic activities, such as industrial, electricity production, transportation, and commercial and residential which can cause environmental degradation with the spread of CO2 carbon dioxide emissions. The growth of a financial sector that supports those economic activities is considered the driver of carbon emissions at the macro level. However, Islam teaches Muslims to avoid corruption on earth including those leading to the rise in carbon emissions. This makes Muslim-majority countries have better concern in dealing with carbon emissions. This study aims to analyze the influences of financial institution index (FII) and financial market index (FII) on carbon dioxide emissions in the Organization of Islamic Cooperation member nations. Using the GMM estimator for a panel dataset of 36 countries between 2000 and 2021, this study unravels that the FII significantly increased carbon emissions in OIC countries. In reverse, FMI contributed to the reduction of carbon emissions in OIC countries. This paper recommends the government set regulations for banking sectors to improve eco-friendly credits for the private sector. It is also recommended that the government enrich the issuance of green securities and strengthen the financial market because it is effective in minimizing carbon emissions.

Does natural resources rent promote carbon neutrality: The role of digital finance

China is regarded as the leading carbon emitter country in the world. Therefore, China has been striving to achieve carbon neutrality. To move towards carbon neutrality targets, there is a need to understand/explore the drivers of carbon emissions. Hence, this empirical investigation discerns the impact of natural resources rent, digital finance, and the combined effect of natural resources rent and digital finance on carbon emissions for China for the period of 1990–2022. This study utilizes a novel augmented ARDL approach. Our empirical results reveal that natural resources escalate carbon emissions during the short-run and long-run. On the contrary, digital finance curbs emissions across time horizons. Further, the combined impact of natural resources and digital finance is negative, indicating that natural resources amid digital finance impede carbon emissions in China. We propose policymakers should introduce policies to discourage natural resource extraction. For this purpose, taxes on natural resource extraction industries may be applied.

Drivers of Carbon Emissions in Kenya: The Perspective of Technology

The empirical studies have portrayed from a different perspective that technology, foreign direct investment (FDI), and economic growth have diverse outcomes on carbon dioxide (CO2) emissions. African economies specifically Kenya are currently threatened with more CO2 emissions for which proper strategies need to be adopted to reduce and mitigate this situation. To address this issue, the Autoregressive Distributed Lag (ARDL) technique was utilized, differentiating between the long-term and short-term effects of drivers of CO2. Granger causality was applied to analyze the causality between the series. The study uses time series data for Kenya from 1990 to 2022. The short-run and long-run results indicate a negative relationship between technology and carbon emissions. Specifically, an increase in technology reduces carbon emissions while an increase in economic growth and FDI inflow increases carbon emissions. The study has identified the negative nexus between technology and carbon emissions in Kenya. This means that technological innovation can increase labour productivity and utilization of resources, thereby reducing carbon emissions. Therefore, Kenya should increase research and utilization of low and efficient carbon technologies to decouple economic growth from environmental pollution. Based on the result, various strategies have been proposed, including using different clean technologies to produce renewable fuels. FDI that complies with environmental regulations, and the use of advanced green and clean technologies can help mitigate the negative impact of technological progress on the environment. Additionally, technological progress, particularly in information and communication technology, will reduce greenhouse gas emissions and improve environmental quality.

Nexus between resource policy, renewable energy policy and export diversification: Asymmetric study of environment quality towards sustainable development

This study examines the environmental performance of the United Arab Emirates (UAE) towards its sustainable development amidst its heavy endowment with natural resources and export diversification. The UAE is among the economies that depend on both export diversification and natural resources for their sustainable economic development. The two mentioned indices (export diversification and natural resources) have been proven to be among the drivers of carbon emissions. Notwithstanding, the UAE has a desert environment with nearly continuous sunlight capable of generating and boosting its renewable energy development. With this potential, it is crucial to create a green energy strategy since renewable energy sources are gaining popularity as alternatives to conventional energy sources. Natural resources and export diversification have garnered mixed reactions in their role towards environmental development. While some researchers report that they induce degradation, others suggest that they mitigate environmental damage. Also, empirically, studies have found renewables such as hydroelectricity, wind and geothermal energy capable of moderating and mitigating the negative impact of carbon emissions on the environment. In this study, data from 1990 to 2019 is analyzed using the nonlinear autoregressive distributed lag (NARDL) and dynamic ordinary least squares (DOLS) scientific methods. The variables of interest are natural resources, export diversification and renewable energy. Findings from NARDL show that export diversification, natural resources and economic growth negatively impact the environment in the UAE by inducing carbon emissions. Specifically, positive and negative shocks of export diversification, natural resources and economic growth to carbon emissions increase and decrease carbon emissions, while positive and negative shocks of renewable energy to carbon emissions decrease and increase carbon emissions. This indicates that the UAE has the potential to achieve its carbon neutrality target with a renewable energy policy.

A Study on the Drivers of Carbon Emissions in China’s Power Industry Based on an Improved PDA Method

The power industry is a major source of carbon emissions in China. In order to better explore the driving factors of carbon emissions in China’s power industry and assist the Chinese government in formulating emission reduction strategies for the power industry, this study applies the improved production-theoretical decomposition analysis (PDA) method to analyze the carbon emission drivers of China’s power industry. This study investigates the impact of energy intensity, per capita GDP, population density, power generation structure, and environmental climate on carbon emissions in China’s power industry in 30 provinces from 2005 to 2020. It was found that the carbon emission ratios of the power sector in all provinces and cities are basically greater than 1, which indicates that carbon emissions in most of the power sectors in the country are still increasing as of 2020. Overall, the effects of potential thermal fuel carbon emission efficiency, potential thermal energy consumption efficiency, the carbon emission efficiency of thermal power generation, economic scale, population density, and annual rainfall change are mostly greater than 1 and will promote the growth of carbon emissions in the power sector. Moreover, the effects of thermal power generation energy efficiency technology, thermal power generation emission reduction technology, power generation structure, and power generation per unit GDP are mostly less than 1 and will inhibit the growth of carbon emissions in the power sector. However, each of these drivers does not have the same degree of influence and impact effect for each province and city. Based on the research results, some policy recommendations are proposed.

Study on spatiotemporal distribution characteristics and driving factors of carbon emission in Anhui Province

Carbon emission is related to global ecological security, and economic development inevitably leads to an increase in carbon emission. In recent years, as a rapidly developing province in China's economy, Anhui Province has experienced significant differences in the spatiotemporal distribution of carbon emission in different regions due to differences in development foundation, urbanization level, population size, industrial structure, etc., providing representative empirical cases for research. Based on the carbon emission data of Anhui Province before the COVID-19, this study used exploratory spatial data analysis method and Geodetector to analyze the spatial and temporal distribution characteristics and drivers of carbon emission in Anhui Province. The study found that (1) the spatial differentiation and spatial correlation of carbon emission in Anhui Province are significant, At the beginning, it shows the characteristics of "high north and low south" and "high west and low east", and then the "core–edge" structure of carbon emission becomes obvious. Carbon emission hotspot areas increase and then decrease, mainly in Hefei, Fuyang and Chuzhou City, etc. The coldspot areas are mainly located in the southern and western mountainous areas, and the degree of aggregation is decreasing year by year. (3) The level of urbanization, economic development and population size are the main driving factors of the spatial variation of carbon emissions, while the industrial structure has the least influence. And most factors produce nonlinear enhancement when spatially superimposed with other factors. (4) The high value areas of economic development, population density, secondary industry structure, and energy intensity are all at high levels of carbon emission, and a combination of factors leads to the creation of high risk areas for carbon emission. The study provides a basis for reducing carbon emission in the next stage of Anhui Province, focusing on key carbon emission areas, and sustainable development.

Digital economy and carbon rebound effect: Evidence from Chinese cities

It is theoretical and practical significance to incorporate the digital economy (DE) into the measurement framework of carbon rebound effect (CRE) for the accurate measurement of the current CRE of Chinese cities. Based on Chinese city-level data from 2011 to 2019, this study innovatively incorporates DE as a driver of carbon emissions, measures the urban CRE in DE through an improved stochastic frontier (SFA) model of carbon emissions, and further explores the formation mechanism of DE to induce the CRE of Chinese cities. When the DE was integrated into the measurement framework of the CRE, the CRE of Chinese cities ranged from 40.7% to 99.1%, with a mean value of 58.4%, indicating that the actual carbon reduction in Chinese cities under the DE was only approximately 40% of that expected. Meanwhile, the CRE of Chinese cities is characterized by cyclical fluctuations and a spatial distribution pattern of “inland to coastal decreasing,” and the polarization effect gradually appears. It is worth noting that the DE significantly contributed to CRE. It will enhance energy efficiency and promote economic growth, which will increase energy consumption through the “substitution effect,” “income effect,” and “output effect,” thus inducing and expanding the urban CRE.

Carbon neutral projections of public buildings in China under the shared socioeconomic pathways: A tertiary industry perspective

As the energy demand of public building (PB) in China continues to increase, it is meaningful for forecasting the future trend of carbon emissions (CE) of PB. This study analyzes the drivers of CE that affect the operational stage of PB in China by the Generalized Divisia Index Method (GDIM), which considers the effect of the output value and structural transformation of the tertiary industry and makes a scenario prediction of the future carbon-neutral trajectory of CE in PB under the Shared Socioeconomic Pathway (SSP). The results show that from 2010 to 2020, the added value of the tertiary industry (AVTI) is the strongest positive driver of CE from PB (31.912% of the total contribution), which correspond to a cumulative contribution of 212.3 million tons (MtCO2) in 11 years. Regulating the CE per unit of the AVTI is the key indicator in reducing CE from PB, with a cumulative reduction of 130.1 MtCO2. The results of the five scenarios under the SSP framework differ significantly, with SSP1 peaking first in 2026 at 884.5 MtCO2 (876.2–893.9 MtCO2) and SSP5 peaking latest in 2049 at 1372.3 MtCO2 (1362-1389.7 MtCO2). SSP1 yielded the smallest projection in 2060 with 189.8 MtCO2 (185.4–194.3 MtCO2). The findings could help public buildings to implement carbon reduction and provide more scientific and rational decision support for possible paths to achieve carbon neutrality in China.

Spatiotemporal patterns and drivers of carbon emissions from municipal solid waste treatment in China

Reducing carbon emissions from municipal solid waste (MSW) treatment is non-negligible for China to meet its “carbon peaking and carbon neutrality” targets. It is critical to objectively evaluate the spatiotemporal patterns and drivers of carbon emissions from MSW treatment. This study estimates the carbon emissions from MSW treatment across 30 Chinese provinces from 2011 to 2020. The joint approach LMDI-PDA model is further used to refine the impact of policy on carbon emission changes from technical and efficiency perspectives, while considering the socio-economic factors. The results showed that carbon emissions from MSW treatment grew significantly until peaking at 202.05Mt CO2e in 2017 and then stabilized, finally dropping to 165.10 Mt CO2e in 2020 due to the impact of COVID-19. Compared with the “12th Five-Year Plan” period, the MSW emissions intensity declined significantly during the “13th Five-Year Plan” period, indicating the effective implementation of waste emission control measures. Furthermore, the slowdown in the growth of national emissions was primarily driven by technological advances in waste treatment. Technical efficiency change effect, MSW generation intensity effect, economic scale effect, and population scale effect impeded national emissions decline. Since the performance of various drivers varied greatly in different provinces, a cluster analysis was conducted to provide policy recommendations in provinces with similar characteristics. Both the methods and results of this study can provide better decision-making support for national and provincial carbon emissions control policies targeting MSW treatment.

The drivers of carbon emissions in China: the perspective of fiscal decentralization

Fiscal decentralization determines government behavior considerably by being a major factor influencing China's carbon emissions. We analyze the impact of fiscal decentralization on carbon emissions on basis of the dataset of 30 Chinese areas for the period 2008 to 2020. We reveal that fiscal decentralization significantly raises carbon emissions, but worsens carbon emission efficiency. The impact of fiscal decentralization on carbon emissions varies significantly by geographical location and economic development level shocks. Local governments increase the expenditure on economic services while reducing the expenditure on basic public services as a result of the fiscal decentralization scheme, which is an important reason why the carbon emission level cannot be effectively improved. As such, it is necessary to keep pace with the times, and build a service-oriented government, and capitalize on the capabilities of local governments in terms of their service duties so as to maximize carbon reduction.

CO2 Emissions Decoupling from Added Value Growth in the Chemical and Pharmaceutical (CHPH) Industry in Nigeria

Nigeria's in its Third National Communication (NTNC) current emissions estimate stipulate that its emissions intensities will continuously increase till 2030, and mitigations measures may not be deep and adequate to meet the upper range of its national reduction goals. Analysing the decoupling states with industrial added-value (IAV) growth, carbon emissions (CE) and the driving forces from a firm-level perspective is critical for the Nigerian state to actualised its 2030 emission reduction objective. Based on the Logarithm Mean Divisia Index (LMDI) procedure and the Tapio index approach, the drivers of CE in Nigeria's chemical and pharmaceutical (CHPH) industry were decomposed, and the decoupling states were measured between 2000 and 2020. The results show that CE increased from 4228.3 Mt in 2000 to 22220.7 Mt in 2020, an approximately 4.3% increase. The IAV growth in this period increased by 1.667%, while the coefficient of emission contracted in 2009 with an average progression rate of 4.4%. The decomposition analysis shows that the most influencing factors of CE were the change in the energy mix (∆EMIX) and energy intensity (∆EI) effects. In contrast, the carbon emissions coefficient (∆CI) effect was the significant driver that reduced CO2 emission. Two decoupling states were revealed: expansive negative decoupling (END) and strong negative decoupling (SND). Conversely, overall, the CE of Nigeria's CHPH industry demonstrated an expansive negative decoupling state with IAV growth. This suggests that the industry's energy consumption increased faster than value-added, with the resultant effect of emissions on the environment. However, the study made clear recommendations for low-carbon policy and environmental sustainability.

Analysing the Effect of Energy Intensity on Carbon Emission Reduction in Beijing.

Beijing has experienced rapid economic development since the reforms and opening up. However, the traditional development model based on excessive energy consumption has posed great challenges to the ecological environment. To curb environmental degradation and achieve sustainable social development, Beijing has proposed to achieve carbon neutrality by 2050. As an important indicator of energy consumption, it is necessary to clarify how energy intensity (EI) affects carbon emissions (CE) to achieve carbon neutrality in Beijing by 2050. This study first decomposes the drivers of CE in Beijing from 2010 to 2020 using the logarithmic mean Divisia index (LMDI) method and comparatively analyses the impact of EI on CE. Then, the spatial Dubin model (SDM) is used to analyse the spatial spillover effect of EI on CE at the regional level. Finally, the macro moderating role of economic development in the effect of EI on CE is analysed. The results show that the effect of EI has been the main driver of CE reduction in Beijing. Among the industrial sectors, manufacturing and transportation have had the greatest success in reducing CE through EI reduction. At the regional level, there is a spatial spillover effect of EI on CE, and the effect of carbon reduction through the spillover effect of EI is greater than the direct effect of EI. Economic factors have an enhanced moderating effect on the process of EI affecting CE, and this moderating effect has threshold properties.

Regional disparities and drivers of carbon emissions in China: A temporal-spatial production-theoretical decomposition analysis.

Given the increasing interest in keeping global warming below 1.5°C, carbon emissions reduction has become a hot topic. However, the regional disparities and the driving factors were not paid enough attention. This article established an indicator to describe the catch-up effort of different regions and proposed a temporal-spatial production-theoretical decomposition model using meta-frontier and global-frontier to capture the driving forces of the catch-up effort of different provinces to benchmarking provinces. The new model was applied to analyze China's regional carbon emissions during 2007 to 2018. The main findings from the empirical study are: (1) Overall, the regional carbon emissions and their spatial variation kept increasing during the study period. (2) Economic activity, potential carbon factor, carbon-abatement technology efficiency and regional carbon-abatement technology gap were the main drivers. (3) The improvement efforts of carbon-abatement, energy-saving technical efficiency, and potential energy intensity were the dominant factors inhibiting the growth of carbon emissions. (4) The improvement efforts of advanced technology and potential energy intensity helped to reduce the regional gaps, but their impacts varied considerably across regions in China.

Simulation of carbon peaking process of high energy consuming manufacturing industry in Shaanxi Province: A hybrid model based on LMDI and TentSSA-ENN.

To achieve the goals of carbon peaking and carbon neutrality in Shaanxi, the high energy consuming manufacturing industry (HMI), as an important contributor, is a key link and important channel for energy conservation. In this paper, the logarithmic mean Divisia index (LMDI) method is applied to determine the driving factors of carbon emissions from the aspects of economy, energy and society, and the contribution of these factors was analyzed. Meanwhile, the improved sparrow search algorithm is used to optimize Elman neural network (ENN) to construct a new hybrid prediction model. Finally, three different development scenarios are designed using scenario analysis method to explore the potential of HMI in Shaanxi Province to achieve carbon peak in the future. The results show that: (1) The biggest promoting factor is industrial structure, and the biggest inhibiting factor is energy intensity among the drivers of carbon emissions, which are analyzed effectively in HMI using the LMDI method. (2) Compared with other neural network models, the proposed hybrid prediction model has higher accuracy and better stability in predicting industrial carbon emissions, it is more suitable for simulating the carbon peaking process of HMI. (3) Only in the coordinated development scenario, the HMI in Shaanxi is likely to achieve the carbon peak in 2030, and the carbon emission curve of the other two scenarios has not reached the peak. Then, according to the results of scenario analysis, specific and evaluable suggestions on carbon emission reduction for HMI in Shaanxi are put forward, such as optimizing energy and industrial structure and making full use of innovative resources of Shaanxi characteristic units.

Seeking Spatiotemporal Patterns and Drivers of Carbon Emissions from Municipal Solid Waste Treatment in China

Seeking Spatiotemporal Patterns and Drivers of Carbon Emissions from Municipal Solid Waste Treatment in China

Accounting and drivers of carbon emission from cultivated land utilization in Northeast China.

With the continuous development of China's society and economy, the breadth and depth of cultivated land resources development and utilization have been continuously expanded, while the production efficiency and scale of cultivated land gradually have been improved. There were rapidly increases of carbon emission induced from cultivated land management, such as the agricultural inputs and energy consumptions. Taking carbon emission from cultivated land utilization in Northeast China as the research object, we determined the carbon emission accounting framework system according to the life cycle method. Based on estimation results of carbon emissions from 1979 to 2015 in Northeast China, the driver factor system affecting agricultural carbon emissions was constructed using logarithmic mean Divisia index model and the influence mechanism of cultivated land carbon emissions in Northeast China was deeply explored. The results showed that total carbon emission from cultivated land utilization in 2015 was 21.9% higher than that in 1979 and carbon emission intensity in 2015 was 1.54 t·hm-2 lower than that in 1979. Soil management and agricultural inputs were the main sources of carbon emissions from cultivated land accounting for 83.6% of the total carbon emissions. The increases of land productivity and science and technology fund allocation rate were found to be the driving factors of carbon emission from cultivated land utilization. On the contrary, the reduction of input/output ratio, cultivated land area per capita and intensity of science and technology investment decreased carbon emission from cultivated land utilization.

Study on Global Industrialization and Industry Emission to Achieve the 2 °C Goal Based on MESSAGE Model and LMDI Approach

The industrial sector dominates the global energy consumption and carbon emissions in end use sectors, and it faces challenges in emission reductions to reach the Paris Agreement goals. This paper analyzes and quantifies the relationship between industrialization, energy systems, and carbon emissions. Firstly, it forecasts the global and regional industrialization trends under Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway2 (SSP2) scenarios. Then, it projects the global and regional energy consumption that aligns with the industrialization trend, and optimizes the global energy supply system using the Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) model for the industrial sector. Moreover, it develops an expanded Kaya identity to comprehensively investigate the drivers of industrial carbon emissions. In addition, it employs a Logarithmic Mean Divisia Index (LMDI) approach to track the historical contributions of various drivers of carbon emissions, as well as predictions into the future. This paper finds that economic development and population growth are the two largest drivers for historical industrial CO2 emissions, and that carbon intensity and industry energy intensity are the top two drivers for the decrease of future industrial CO2 emissions. Finally, it proposes three modes, i.e., clean supply, electrification, and energy efficiency for industrial emission reduction.

Drivers of carbon emissions in China’s tourism industry

This manuscript examines the driving forces of carbon emissions in China’s tourism industry. Tourism carbon emissions are estimated by constructing China’s Economic-Environmental Accounts (EEA). Analysis is divided into five-time intervals and specifically examines intensity, scale, structure, and technology. Following index and structural decomposition methods, changes in tourism carbon emissions were segmented into sixteen economy-wide and tourism-specific driving forces. Results demonstrate that direct and total tourism carbon emissions compose 0.7% and 2.7% of total carbon emissions in China. Analysis revealed the positive driver of tourism emissions was domestic tourists, representing 140.4% increase in direct and 263.4% increase in total tourism carbon emissions. Modelling identified energy intensity as the main negative driver in total and direct tourism carbon emissions, especially for national economic sectors (−208.6%) and non-transport tourism sectors (−33.8%). Future research should focus on the measurement and implementation of mitigation policies for domestic tourism emissions.

Drivers of carbon emissions in Turkey: considering asymmetric impacts.

This study investigates the impacts of income, (renewable and non-renewable) energy consumption, trade, and financial development on carbon dioxide emissions in Turkey for the 1965-2015 period by employing thenon-linear autoregressive distributed lag method. Results show that non-renewable and renewable energy consumption, and trade openness have asymmetric impacts on pollution in long-run, while only renewable energy consumption has asymmetric impact on emissions in short-run. Results further reveal that the Environmental Kuznets Curve hypothesis is not valid in Turkey. Moreover, both financial development and trade positively affect emissions. Additionally, in long-run, positive shocks in renewable and non-renewable energy consumption increase emissions, but the impact of renewable energy consumption is infinitesimally small compared to the impact of non-renewable energy consumption. However, negative shocks in renewable energy consumption increase emissions, whereas negative developments in non-renewable energy consumption decrease emissions. Further, in short-run, positive developments in renewable energy consumptiondecrease emissions, and negative developments in non-renewable energy consumption have the same influence on emissions. In accordance with the findings, some policy suggestions are proposed.