Energy Intensity Effect Research Articles

Decomposition analysis of China's chemical sector energy-related CO2 emissions: From an extended SDA approach perspective

The excessive growth of carbon emissions (CO2E) from industrial energy use not only exacerbates global warming and severely curbs the sustainable development of the economy and society. As a high energy-consuming sector second only to the fossil energy division, the power and heavy division, China's chemical industry should have received more attention for its CO2E. However, there are limited literatures on energy CO2E in China's chemical sector at present. Based on this fact, this current paper uses the energy utilization approach, the input–output analysis approach, and the extended structural decomposition method to evaluate the energy-related CO2E of China's chemical sector from 2007 to 2017. (1) China's chemical sector energy-related CO2E showed a trend of first growth and then a slow decline, demonstrating that the rapid growth of China's chemical sector energy-related CO2E has been effectively controlled; However, it should be noted that the chemical industry is still dominated by high-CO2E energy-related CO2E at the current stage. (2) Input structure and energy intensity effects have a reduced influence on the growth of energy-related CO2E in China's chemical sector. This is due to upgrading energy use technology and optimizing the generalized technology progress rate in the chemical sector. (3) Energy structure and final demand effects have encouraged the growth of the chemical sector's energy-related CO2E. It shows that the industrial system's demand for chemical products is constantly expanding, and the chemical products still have the characteristics of high carbonization. Also, the chemical sector's supply-side energy utilization structure has not been significantly enhanced.

The status quo of the EU transport sector: Cross-country indicator-based comparison and policy evaluation

Greenhouse gas (GHG) emissions are declining in all major sectors in the European Union, with the exception of one sector that has seen a significant increase in GHG emissions over the past decade - transport sector. The European transport sector faces a significant challenge in achieving the decarbonization goal set by the European Green Deal. Although the European Commission is planning to introduce a set of EU-level measures, such as the inclusion of road transport in the EU Emissions Trading System, the progress of greening the transport sector has so far largely depended on national policies. To understand whether European countries are committed to rapidly adapt to low-carbon transport systems, it is crucial to evaluate what progress have national policies achieved so far in moving towards a sustainable transport system. This study used a three-level assessment of transport sector sustainability across European countries. First, this study assessed the overall sustainability level of the transport sector in all European Union Member States and the United Kingdom using the composite sustainability index method for cross-country comparison. Countries were compared using 15 transport indicators grouped into four dimensions (mobility, sustainability, innovation, and environment) based on the latest available data from 2017 obtained from Eurostat, European Commission, and Odysee-Mure databases. Second, in order to identify the dynamics of progress, Logistic Mean Division Index (LMDI) decomposition analysis was conducted to assess the changes in greenhouse gas (GHG) emissions from the transport sector over ten years, taking into account the five main factors: emission intensity effect, renewable energy (RES) transition effect, energy intensity effect, economic growth effect, and population growth effect. Third, to analyse what has impacted the sustainability of the transport sector in countries, the study highlighted several key policies (directions) for decarbonizing transport from the analysis outlining the good practice policies from the best performing countries. The combined results of the composite sustainability index and decomposition analysis revealed significant differences between Nordic, Western, and Eastern European countries, suggesting that different approaches should be taken in developing effective sustainability policies for transport.

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.

Effect of Energy Intensity and Co2 Emissions on Sustainable Development in Nigeria

Effect of Energy Intensity and Co2 Emissions on Sustainable Development in Nigeria

Towards a decoupling between regional economic growth and CO2 emissions in China's mining industry: A comprehensive decomposition framework

As the underlying industry for energy production, the mining industry contributes to economic growth while consuming large amounts of energy and generating CO2 emissions. This paper adopts the Tapio model to investigate the decoupling process of China's mining industry at the national, regional, and provincial levels. A comprehensive decomposition framework is constructed to identify the drivers of decoupling. The results reveal that: (1) The mining industry has performed relatively well in decoupling, with weak decoupling being the dominant state and a strong decoupling achieved. The main contributors are the output scale effect (YS) and the potential energy intensity effect (PEI). There are fluctuations in technology and efficiency-related effects. (2) There is a disparity in decoupling status and driving mechanisms between regions. The eastern region shows the best decoupling performance, benefiting from PEI. The decoupling status in the central region fluctuates considerably, with YS being the primary influence. The western region mainly presents a weak decoupling state, with the boosting effect of output technology increasing year by year. (3) Weak decoupling is a common inter-provincial state. Spatial heterogeneity exists in the driving effects across provinces. Policy recommendations for decoupling progress and low carbon development in the mining industry are presented.

Measurement and Driving Factors of Carbon Emissions from Coal Consumption in China Based on the Kaya-LMDI Model

As the top emitter of carbon dioxide worldwide, China faces a considerable challenge in reducing carbon emissions to combat global warming. Carbon emissions from coal consumption is the primary source of carbon dioxide emissions in China. The decomposition of the driving factors and the quantification of regions and industries needs further research. Thus, this paper decomposed five driving factors affecting carbon emissions from coal consumption in China, namely, carbon emission intensity, energy structure, energy intensity, economic output, and population scale, by constructing a Kaya-Logarithmic Mean Divisia Index (Kaya-LMDI) decomposition model with data on coal consumption in China from 1997 to 2019. It was revealed that the economic output and energy intensity effects are major drivers and inhibitors of carbon emissions from coal consumption in China, respectively. The contribution and impact of these driving factors on carbon emissions from coal consumption were analyzed for different regions and industrial sectors. The results showed that carbon emissions from coal consumption increased by 3211.92 million tons from 1997 to 2019. From a regional perspective, Hebei Province has the most significant impact on carbon emissions from coal consumption due to the effect of economic output. Additionally, the industrial sector had the most pronounced influence on carbon emissions from coal consumption due to the economic output effect. Finally, a series of measures to reduce carbon emissions including controlling the total coal consumption, improving the utilization rate of clean energy, and optimizing the energy structure is proposed based on China’s actual development.

Exploring the CO2 emissions drivers in the Nigerian manufacturing sector through decomposition analysis and the potential of carbon tax (CAT) policy on CO2 mitigation

The CO2 emissions trend and their reduction potential in the Nigerian manufacturing sector from 2010 to 2020 were studied. The Logarithmic Mean Divisia Index was applied to decompose the change in CO2 emissions into pre-set factors: carbon intensity effects, firm energy intensity effects, cost structure effects, asset-turnover effect, asset-to-equity effect, equity-funded production effect and productive capacity utilization. The results show that the change in emissions increased by 1668times {10}^{12} GJ between 2010 and 2020. Energy intensity and equity-funded production were the leading drivers of increased emissions, while productive capacity utilization reduced emissions. The CO2 emissions increased throughout the study, except for a few periods. Without a carbon tax policy, the results show that firm-level drivers increased CO2 emissions in the business-as-usual scenario. However, under the 5% carbon tax (CAT) policy scenario on energy consumption, there was a reduction in CO2 emissions between 2010 and 2020. Furthermore, a CAT policy of 5% on energy consumption reduced CO2 emissions by 22%. A further implication of CAT policy, given its interaction with firm-level drivers, resulted in lowering CO2 emissions in the interactional scenario. The findings indicate productive capacity utilization, equity-funded production, and CAT impacted CO2 emissions variation.

Drivers of electricity GHG emissions and the role of natural gas in mexican energy transition

In the last three decades, the high growth of natural gas as an energy source for Mexican electricity production was the most significant change in the sector. Natural gas went from being the source of 7% of electricity in 1990 to 62.3% in 2020. A co-dependence of electricity and natural gas systems has been established and is increasing. Is this fact consistent with the objective of decarbonizing the electricity sector? We study this question through a decomposition analysis of electricity Greenhouse Gas (GHG) emissions in Mexico between 1990 and 2015. We use a Logarithmic Mean Divisia Index (LMDI) to quantify the changes of electricity GHG emissions related to activity, carbon coefficient, structure, and energy intensity effects. Activity effect was the most significant driver of GHG emissions growth, structure and energy intensity effects contributed to limiting that growth, while the carbon coefficient effect had a small contribution to mitigating GHG emissions. From these results, we raise concerns about the role of natural gas, which crowd-out renewable energy and could lead to carbon lock-in and stranded assets in the long term. In addition, Mexico's energy system relies heavily on U.S. natural gas. In light of these concerns, we concluded that an energy policy aiming towards a low-carbon energy system should consider the composition “natural gas + renewable energies + energy efficiency”.

How Does Energy Consumption and Economic Development Affect Carbon Emissions? A Multi-Process Decomposition Framework

Against the background that climate warming has become a global challenge, exploring the factors that drive carbon emissions change is important to achieve emission reduction targets. Because of the differences in economic development, resource endowment, and historical accumulation, different countries generally have significant technological heterogeneity in the carbon generation process. Therefore, the heterogeneity-related factors should also be understood, which can help policy making and responsibility attribution more accurate. As such, this study developed a meta-frontier-based production–theoretical decomposition analysis method to track the progress of carbon emission change in 42 countries during 2012–2019 with production heterogeneity between the countries taken into account. The empirical study draws the following three meaningful conclusions: firstly, the carbon emission process of different countries has clear technological heterogeneity, mainly reflected in aspects of their energy-use efficiency and energy-use technology. Secondly, the decomposition analysis results showed that the potential energy intensity effect and the economic activity effect played the dominant role in driving and reducing carbon emissions, respectively. Additionally, this conclusion is right for all types of countries. Thirdly, the attribution analysis showed that different types of countries have significantly different contributions to the influencing factors of carbon emission changes, among which countries with large energy consumption and large economies need to take more responsibility for emission reduction.

Decoupling trend and emission reduction potential of CO2 emissions from China's petrochemical industry.

This paper aims to study the decoupling status and emission reduction potential of China's petrochemical industry from 1996 to 2019. Firstly, the IPCC method is used to calculate the CO2 emissions of the petrochemical industry in China, then the logarithmic mean Divisia index (LMDI) method is used to identify the influencing factors of CO2 emissions, then the decoupling index is constructed to analyze the dependence of economic development on CO2 emissions, and finally the emission reduction potential model is established by using the influencing factors to reflect the CO2 emission reduction potential of the petrochemical industry. The results reveal that (1) the CO2 emissions can be divided into two stages of slow decline (1996-2000), (2015-2019), and one stage of rapid growth (2000-2015). (2) The energy intensity effect is the most effective factor to restrain CO2 emission, the economic growth effect is the key factor to promote CO2 emission. (3) From 1996 to 2019, there was a weak decoupling relationship between CO2 emission of petrochemical industry and economic development. Strong decoupling only occurred in 1996-2000 and 2015-2019. The CO2 emissions show only three decoupling score: I, II, and III. (4) CO2 mitigation occurred in four sub periods: 1996-2000, 2005-2010, 2010-2015, and 2015-2019. Therefore, the government should establish an energy-saving and environment-friendly industrial production system, intensify the use of clean energy, and optimize the labor force structure. It not only effectively strengthens the decoupling between the petrochemical industry and economic development, but also provides an empirical example for the carbon emission reduction and economic sustainable development of the petrochemical industry in other countries in the world.

Exploring the driving forces of CO2 emission changes in Chinese cities: A production-theoretical decomposition analysis

Analyzing the forces driving CO2 emissions in cities could provide valuable information for carbon reduction policies in China. This study uses an improved production-theoretical decomposition analysis to evaluate the CO2 emissions of 282 cities in China during 2003–2017. The empirical results show that the scale, energy intensity, and desirable output productivity effects contributed to about 15.03%, 3.64%, and 2.3% growths in CO2 emissions on average, respectively, while the potential CO2 emission and undesirable output productivity effects were responsible for 5.81% and 5.72% reductions in CO2 emissions. By classifying the sample cities and analyzing them further, it was found that the potential CO2 emission effect has a stronger inhibitory impact in resource-based cities. However, the promoting effects of the scale effect is more obvious in non-resource-based cities. From a spatial distribution perspective, the potential CO2 emission effect has a more obvious inhibitory role, and the energy intensity effect is a strong measure for controlling the growth of CO2 emissions in the eastern region. However, the contribution of the scale effect to CO2 emissions is more pronounced in the western region. In addition, we found that the desirable output productivity effects had a suppressive effect in the eastern region and facilitating effects in the central and western regions. The undesirable output productivity effect had a suppressive effect on the growth of CO2 emissions in all three regions, but the suppressive effects were more pronounced in the eastern region.

The decoupling relationship between CO2 emissions and economic growth in the Chinese mining industry under the context of carbon neutrality

Mining industry emission reduction is an important way to achieve the 2060 carbon neutrality target in China. Decoupling economic development from CO2 emissions is the main strategy to reduce emissions from the mining industry. However, no studies have investigated the decoupling state and drivers of the Chinese mining industry. This paper is based on panel data of the mining industry and its subsectors during 2002–2016. The Tapio decoupling model was constructed to analyze the decoupling state of economic development and CO2 emissions. The LMDI model was developed to decompose the drivers of the decoupling state. The results show that: (1) The CO2 emission intensity of the mining industry decreased significantly from 1.63 tons per 10,000 yuan in 2002 to about 0.27 tons per 10,000 yuan in 2016. (2) The Chinese mining industry shows an inverted U-shaped decoupling relationship between CO2 emissions and economic growth. Specifically, the change trend of decoupling state of Mining and Washing of Coal (MWC) also presents an inverted U-shape. Mining and Processing of Nonmetal Ores (MPNO) performs the best during the study period. Extraction of Petroleum and Natural Gas (EPNG) shows the largest change in decoupling state and the worst performance. (3) Overall, the economic output effect (CG) is the most important factor that promotes CO2 emissions and inhibits decoupling. The energy intensity effect (EI) is the main contributor to suppressing CO2 emissions and promoting decoupling. The energy structure effect (ES) is the most promising influencing factor. (4) There is variability in the role played by different 21 factors in the five subsectors. Specifically, EI inhibits decoupling in MWC but promotes decoupling in EPNG、Mining and Processing of Ferrous Metal Ores (MPFMO) and MPNO. ES inhibits decoupling in MWC and MPFMO but promotes decoupling in MPNFMO, while it has little effect on decoupling in EPNG and MPNO. Based on the results, comprehensive policy recommendations are made to achieve decoupling economic growth from CO2 emissions in the Chinese mining industry.

A multi-level characteristic analysis of urban agglomeration energy-related carbon emission: A case study of the Pearl River Delta

Urban agglomeration is identified as the important geographic units of country development while contributing immensely to carbon emission. This paper explored energy-related carbon emission characteristics of the Pearl River Delta (PRD) - the typical urban agglomeration in China, from cities and sectors perspectives. By conducting LMDI model, this paper decomposed carbon emission by sector and by cities to illustrate the key impact factors in the PRD, explored the reasons for the change of driving effects in urban agglomeration in view of regional difference. It concludes that, most cities carbon emission rebound around 2017, while carbon emission sharply decreased. There are differences in key reduction industries in different cities, while production and supply of electric power and transport sector had negative impact on reduction processes in most cities. The significant inhibition effect of energy intensity is mainly attributed to the great decrease of energy intensity in main industries reduction cities, such as Shenzhen, Guangzhou, Dongguan and Foshan. Based on the characteristics of carbon emission and social and economic development of the PRD, this paper puts forward specific suggestions and measures for differentiated emission reduction in urban agglomeration, and provides a new idea for the governance of carbon emission in urban agglomeration.

Exploring the driving forces and scenario analysis for China's provincial peaks of CO2 emissions

Understanding the drivers and peaks of CO2 emissions at the provincial level plays a crucial role in achieving the goals of China's CO2 emissions peak by 2030. This research combines the spatial-temporal Logarithmic Mean Division Index with scenario analysis to empirically explore the drivers and peaks of CO2 emissions in 30 provinces in China during 1997 and 2020, considering COVID-19 effects. The results show that the energy structure has replaced the energy intensity as the main factor of emission reduction in 2020. The CO2 emission driving mechanism at the provincial level is different from that at the country level. The population restrains the CO2 emissions in Heilongjiang, Sichuan, and Guizhou. The energy structure increases CO2 emissions in Hainan, Ningxia, Shanxi, and Xinjiang. The role of driving factors to CO2 emissions varies greatly among provinces. The population effect is strong in Shandong, Guangdong, and Henan. The economic effect is significant in Shanghai, Jiangsu, and Tianjin. The energy intensity effect is remarkable in Shanxi, Ningxia, and Inner Mongolia. The energy structure effect is profound in Shanxi, Inner Mongolia, and Henan. Based on our findings, China's CO2 emissions peak will occur in 2030 and 2025 under baseline and green development scenarios. Many provinces have already reached their peaks, including Chongqing, Yunnan, Beijing, Tianjin, Qinghai, Shanghai, Jilin, Hubei, Heilongjiang, Hebei, Sichuan, Anhui, Guizhou, and Henan. However, Xinjiang and Shanxi will not reach their peaks by 2030. Based on the findings, this paper put forward several policy implications.

Impact of the differences in carbon footprint driving factors on carbon emission reduction of urban agglomerations given SDGs: A case study of the Guanzhong in China

• The driving mechanism of CF based on SDGs is revealed. • There is a critical point in the population agglomeration effect on CF. • The effects of energy structure and energy intensity on CF differ significantly. • The “ strong decoupling ” state of CF and economic growth is unstable. • Urban agglomerations plays an important role in carbon emission reduction. Urban agglomerations (UAs) are important actors in carbon emission reduction (CER) of China, but with the acceleration of urbanization, the increasing carbon footprint (CF) seriously hinders UAs to achieve the Sustainable Development Goals (SDGs). Taking the Guanzhong urban agglomeration (GUA) as a case, the CF are measured by IPCC emission inventory method. It is analyzed that the temporal and spatial evolution characteristics of CF. The logarithmic mean Divisia index model and Tapio decoupling model are used to explore the effects and difference changes of the CF driving factors and analyze the decoupling relationship between CF and economic growth. The results show that the CF of the GUA increased from 161.60 to 274.02 million tons of carbon dioxide equivalent in 2007-2018, which is an increase of 71.25%. Urban expansion, industrial structure and economic growth promoted the increases, and population agglomeration promoted the decreases. The effects of the energy structure and energy intensity on the CF changes are significantly different among cities. The decoupling state of CF and economic growth is unstable. The study provides the SDGs-oriented research framework on CF driving factors for developing regions and helps improve the adaptability of CER policies in UAs.

Decoupling China’s mining carbon emissions from economic development: Analysis of influencing factors

Mining provides basic materials and energy for human life and supports economic and social prosperity and development. The decoupling of mining carbon emissions from economic development is an important way of achieving China’s carbon peaking and carbon neutrality goals. This study uses the Tapio decoupling model to measure the relationship between China’s economic development and carbon emissions from 2001 to 2018. It analyzes the overall industry as well as its subdivisions and identifies the factors driving carbon emissions with help from the improved Kaya identity and LMDI decomposition models. The results show that, except for the unstable situation in the oil and natural gas mining industry, the other mining divisions have attained strong decoupling and have become stable, showing a continuous positive trend. On the whole, the mining product smelting and processing industry has achieved a major transformation, moving from negative decoupling to weak decoupling, but there are great differences between different sub-sectors. The overall consumption of China’s mining products, and the incremental carbon emissions have continued to decline, while economic development has shifted from inefficient expansion to high-quality economic development, although without reaching the ideal state. The economic factor and energy intensity effects are the key factors in increasing and restraining carbon emissions, respectively, and their influence should not be ignored. This study aims to provide a decision-making basis for China’s mining industry, that it might carry out carbon emission reduction planning, and promote the clean and efficient construction of the industry and the green and high-quality development of the economy.

Analysis of urban carbon emission efficiency and influencing factors in the Yellow River Basin.

The Yellow River Basin is an energy-rich area. The low-carbon development of the Yellow River Basin is one of the ways to achieve ecological protection and high-quality development. Based on panel data from 2007 to 2019, the dynamic and static carbon emission efficiencies of 98 cities in the Yellow River Basin were calculated using the Undesirable Super Slacks-based model and the Global Malmquist-Luengerber index method, respectively, and the spatial effects of influencing factors were analyzed using the spatial Dubin model. Main conclusions are as follows: (1) The carbon emission efficiency of cities in the Yellow River Basin is different, and only a small numbers of cities have reached the effective state. (2) The main factor for the increase in the overall carbon emission efficiency of the whole Basin is technological progress, and the main factors for the improvement of carbon emission efficiency of different cities are various. (3) The direct effects of economic development level and industrial structure upgrading are significantly positive, the direct effects of technological development level, energy intensity, and opening up are significantly negative, and the indirect effects of population density are positive. The conclusions are of great significance for promoting low-carbon development, ecological protection and high-quality development in the Yellow River Basin.

Energy, environmental degradation, and health status: evidence from South Asia.

Energy is considered a vital factor of economic growth that contributes to improve quality of life and health status. However, global warming, climate change, and environmental degradation are due primarily because of energy emissions, whereas environmental degradation is detrimental to health. Since one-fifth of the population lives in South Asia, it is necessary to analyze the impact of energy and environmental degradation on health status in this region. For this purpose, health status in South Asia is proxy with health expenditure, life expectancy, and infant mortality, and this study investigates the effect of energy intensity, income, and carbon emissions on health status, whereas urbanization is considered a control variable. The cointegration test indicates South Asia's long-term health status factors are energy intensity, income, carbon emissions, and urbanization. Long-run results suggest that energy intensity and income improve health status as these factors reduce health expenditure, improve life expectancy, and decrease infant mortality. Environmental degradation not only increases health expenditure but also hinders life expectancy and increases mortality. Moreover, an increase in income diminishes health expenditure and is responsible for high life expectancy and low mortality in South Asia.

Driving factors and key emission reduction paths of Xinjiang industries carbon emissions: An industry chain perspective

As a typical resource-based region, Xinjiang is under increasing pressure to reduce carbon emissions. The ability to identify the drivers of carbon emissions in the industry and mapping out key emission reduction paths are core components of achieving carbon emission reduction in Xinjiang. This research incorporates energy and environmental factors, constructs a hybrid input–output model of “energy-environment-economy,” and uses the environmental input–output structural decomposition (EIO-SDA) and structural path decomposition (SPD) methods to analyze the drivers of carbon emissions from energy consumption and key emission reduction paths in Xinjiang. First, it shows that the economic-scale effect and the energy-intensity effect are the biggest facilitators of carbon emissions and most significant barriers to carbon emission reduction in Xinjiang. Second, capital formation and domestic trade are the primary sources of demand driving changes in carbon emissions in Xinjiang. Third, sectors involving the production and supply of electricity and heat, such as heavy manufacturing and energy industries, including petroleum processing, coking, nuclear, fuel processing, and chemical industries, are the key sectors responsible for carbon emission reduction in Xinjiang. Last, in terms of industry chains, “metal smelting and rolling processing industry/non-metallic mineral products industry—construction industry—fixed capital formation” and “oil and gas extraction industry (S20)—(intermediate sector)—final demand” are the most important paths driving the growth and decline of carbon dioxide (CO2) emissions from Xinjiang industries, respectively. To promote Xinjiang's carbon emission reduction targets, the Xinjiang government should actively improve its energy structure, promote a change in demand growth patterns, and formulate comprehensive management policies for high-carbon-transfer industries according to an industry chain perspective.

Production- and consumption-based carbon emission decoupling and decomposition of the Belt and Road countries

The Belt and Road Initiative (BRI) provides a platform for developing countries with huge growth potentials, which may also face huge carbon emission pressure while achieving rapid economic growth. Given certain similarities in economic patterns and resource endowments, this study aims to trace carbon emission decoupling and decomposition of different countries/regions within the Belt and Road area and provide new insights into the drivers of carbon emission decoupling from both production- and consumption-based perspectives. Based on the multi-regional input-output modelling and Tapio decoupling decomposition, this study quantitatively analyzes the decoupling evolution and decomposition drivers of economic activities and carbon emissions in countries along the Belt and Road. From the results, the production-based carbon emissions of the Belt and Road countries was significantly higher than the consumption-based carbon emissions. The increasing rate in the production-based carbon emissions was also faster than the consumption-based one, with an increasing huge gap between the two sides. Regarding the spatial distribution of carbon emissions, the regions with huge amounts of carbon emissions mainly distributed in Russia, Iran, South Korea, and Saudi Arabia. When compared, the consumption-based carbon emissions of China and Russia were the highest, followed by those of the countries in Central Asia and West Asia. Compared with the production-based side, the decoupling rate of the Belt and Road countries was slower than the consumption-based one. The Belt and Road countries mainly maintained in the weak decoupling status, with the economic effect as the main driver in carbon emission growth, and the energy intensity effect as the dominated contributor in carbon emission reduction. Through exploring the decoupling and decomposition of production- and consumption-based carbon emissions within the Belt and Road countries, this study attempts to provide certain implications for the low-carbon transition and sustainable development within the countries along the Belt and Road.