Differences in Carbon Intensity of Energy Consumption and Influential Factors between Yangtze River Economic Belt and Yellow River Basin
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1. Introduction
Actively combating global warming has become a consensus of all mankind. As the largest developing country, China has actively assumed the responsibility of carbon emission reduction and has put forward the goal of achieving the carbon peak by 2030 and carbon neutrality by 2060, known as the “dual-carbon” goals. Furthermore, the 14th Five-Year Plan emphasizes the implementation of a system prioritizing carbon intensity control, supplemented by overall carbon emission control. This underscores the significance of carbon intensity as a key indicator of carbon performance. And watersheds are the birthplace of human civilization, carrying the profound history of the evolution and development of human economic division of labor. Watershed economic zones are the spatial mainstay of China’s regional economic development, among which the Yangtze River Economic Zone and the Yellow River Basin are important economic regions and ecological barriers in China [1]. With the release of the Outline of the Development Plan for the Yangtze River Economic Belt (2016) and the Outline of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin (2021), the “river strategy” at the national level has been established. As major strategic regions, the Yangtze River Economic Zone and the Yellow River Basin (hereinafter referred to as the two river basins) will account for about 70% of China’s total economic output and carbon emissions in 2020 and will play a pivotal role in China’s overall green economic growth and “dual-carbon” strategy. At the same time, the two river basins exhibit notable differences in economic level and carbon emission characteristics. The Yangtze River Economic Belt functions as a vital waterway connecting China’s east, middle, and west regions, featuring major growth poles: the Chengdu–Chongqing City Cluster, the middle reaches city cluster, and the Yangtze River Delta City Cluster. Conversely, the Yellow River Basin serves as a significant ecological barrier in China, also functioning as an energy basin with ample fossil resources. In 2020, the GDP and CO2 emissions of the 11 provinces in the Yangtze River Economic Zone constituted 46.08% and 27.86% of the national total, while the 9 provinces in the Yellow River Basin contributed 24.71% and 41.09% to the national totals, respectively. It can be seen that the resource conditions, carbon generation paths, and development trajectories differ significantly between the two river basins, both inter-basin and intra-basin. Consequently, there is an urgent need for carbon intensity studies with extended time series and detailed categorization, including an exploration of driving factors and their interaction mechanisms. To this end, this paper explores the overall, inter-basin and intra-basin differences in carbon emission intensity (referred to as carbon intensity) in two major river basins and identifies the factors affecting the differences in carbon intensity in the two basins, aiming at exploring how to realize the path of carbon reduction and emission cuts in the river basins by using the Yangtze River Economic Zone and the Yellow River Basin as examples. This study is a positive exploration of China’s “river strategy” and “dual-carbon strategy”.
Carbon intensity, defined as greenhouse gas emissions per unit of GDP, serves as a key indicator to assess a region’s advancements in optimizing the benefits of energy utilization and reducing associated carbon emissions during its developmental process [2,3]. To date, many scholars have conducted an extensive array of studies addressing regional disparities and the factors influencing carbon intensity.
In the examination of regional disparities in carbon intensity, most scholars typically approach the analysis from two primary dimensions: industry and space. The investigation into the industry dimension delves into agriculture, the service industry, manufacturing, transportation, and more [4,5,6]. It reveals substantial variability in carbon intensity across different industries, with areas of both high and low values exhibiting dynamic changes over time. The exploration of the spatial dimension occurs at various scales, including national, provincial, municipal, and city cluster levels as well as metropolitan areas [7,8,9]. The findings indicate a significant reduction in carbon intensity for national cities and major strategic regions, with notable disparities in the dynamic evolution of carbon intensity across different regions. Moreover, there is a discernible trend of expanding differences between major strategic regions. In investigating regional disparities in carbon intensity, scholars commonly employ research methods such as the Gini coefficient, Theil index, kernel density estimation, and exploratory spatial data analysis. These approaches are utilized to examine intra-regional differences, inter-regional disparities, spatial agglomeration characteristics, spatial correlation, and dynamic evolution patterns of carbon intensity [10,11]. However, there has been less emphasis on studying carbon intensity at the watershed scale. Notably, there is a scarcity of studies that specifically analyze the differences in carbon intensity between two major watersheds from a comparative perspective.
In the exploration of influencing factors contributing to regional differences in carbon intensity, early researchers commonly employed demographic, economic, and technological factors outlined by the IPAT model and the KAYA constant equation as the foundational framework for assessing such factors [12,13,14,15]. As research on carbon intensity in China has deepened, the scope of influencing factors contributing to regional differences has expanded. Notably, demographic structure, economic composition, industrial makeup, technological advancements, energy structure, and government regulation have been recognized as crucial contributors to carbon intensity [16,17,18]. Various studies have adopted distinct empirical methods based on their specific focuses, with many relying on econometric models such as the Kaya constant equation and the LMDI method [19,20,21], or the IPAT or STIRPAT model [16,22]. Additionally, spatial econometric models, including geographically weighted regression, the spatial lag model, the spatial error model, and the spatial Durbin model, have been employed [18,23,24]. Nevertheless, inherent issues of multicollinearity and endogeneity among variables in measurement models are prone to causing objective bias in measurement results. In contrast, the geodetector has the potential to overcome the limitations associated with variable processing in econometric models, providing an objective reflection of the degree of influence exerted by each influencing factor.
In general, research on regional differences in carbon intensity has yielded significant results, yet there remains room for expansion in the following areas: (1) Scope Expansion: The majority of related studies have focused on administrative regions, such as provinces and cities, or economic regions like urban agglomerations [16,18,19]. However, scant attention has been given to the characteristics and differences of carbon intensity within watersheds. This paper specifically concentrates on a comparative study of carbon intensity in the Yangtze River Basin and the Yellow River Basin in China. (2) Methodological Expansion: Despite the adoption of various research methods by scholars, further exploration is required to address inherent issues such as multicollinearity and endogeneity among variables. The use of a geodetector in carbon intensity research is anticipated to overcome these limitations. In this study, the carbon intensity Theil index was constructed to examine overall differences, inter-basin differences, and intra-basin differences in carbon intensity within the two watersheds. Finally, a geodetector was employed to investigate the influencing factors and interaction levels of carbon intensity in the two watersheds.
The potential contributions of this article are outlined as follows: (1) Addressing Research Gaps: Employing a comparative research perspective helps mitigate the inadequacies in watershed carbon intensity research. Given the strategic importance of the two major river basins in China’s economic development and ecological protection, their significant contrast in economic level and carbon emission reduction processes can be better understood through this comparative approach. It elucidates the underlying causes of the carbon intensity gap between the two basins, facilitating the complementation of mutual strengths and expediting their respective carbon emission reduction processes. (2) Methodological Enrichment: The utilization of the geodetector enhances the exploration of factors influencing carbon intensity. By employing the geodetector to analyze these factors, the work avoids potential objective bias in measurement results stemming from the inherent multicollinearity and endogeneity of influencing factors. This approach enables a more precise identification of the influencing factors of carbon intensity in the two major river basins. (3) Practical Implications: The conclusions drawn from this study offer crucial insights for the provinces within the two major river basins in China. These insights can inform the rational formulation of coordinated development policies encompassing the economy, society, and the environment. Such guidance is instrumental in achieving the goals of ecological protection and high-quality development in China’s two major river basins. Furthermore, it holds practical significance for advancing global efforts in carbon reduction and emission reduction within other river basins worldwide.
3. Comparative Analysis of Carbon Intensity in Two Major River Basins
3.1. Comparative Analysis of Carbon Intensity in Two Major Watersheds Based on Time Series Evolution
At the overall level, the carbon intensity in the entire country, the Yangtze River Economic Belt, and the Yellow River Basin generally exhibits a weakening trend from 2000 to 2020 (Figure 2). However, the characteristics of this weakening differ. In absolute terms, the order is “Yangtze River Economic Belt national level (−3.22%) > Yellow River Basin (−1.73%)”. It is evident that the carbon intensity of the Yangtze River Economic Belt consistently surpasses the national average in both absolute value and the rate of decrease. In contrast, the Yellow River Basin registers values lower than the national average in both absolute value and the rate of decrease.
Furthermore, structural disparities in the overall weakening carbon intensity between the two river basins are evident (Figure 3 and Figure 4). The decline in carbon intensity in the Yangtze River Basin primarily resulted from rapid economic growth before 2011; however, post-2011, it was driven by the dual effects of a plateau in total carbon emissions and high-quality economic growth. Conversely, the Yellow River Basin experiences continuous growth in both total carbon emissions and economic volume. The fluctuation in the carbon intensity of the Yellow River Basin, initially increasing and then decreasing, can be attributed to a larger growth rate of carbon emissions compared to economic growth before 2006, resulting in a slight increase in carbon intensity. After 2006, the economic growth rate surpassed the growth rate of carbon emissions, leading to a gradual decline in carbon intensity. Despite the parallel weakening trends in carbon intensity in both basins, the Yangtze River economy is already in the stage of weak decoupling of carbon emissions from the economy, while the Yellow River Basin is still in the stage of synchronized growth of carbon emissions and the economy.
At the sub-basin level, there is a declining trend in the carbon intensity observed in the upper, middle, and lower reaches of the Yangtze River Economic Belt, leading to a gradual convergence of the gaps between these basins (Figure 5). Specifically, the carbon intensity within the Yangtze River Economic Belt follows the order “upstream > midstream > downstream” concerning both absolute values and rates of decline. The respective decreases are 2.98, 2.61, and 178 million tons/billion CNY, with corresponding decline rates of −5.34%, −4.97%, and −0.43%.
In contrast, the carbon intensity in the upstream, midstream, and downstream of the Yellow River Basin exhibits a decreasing trend in absolute values but with a more modest reduction, resulting in a trend toward convergence, albeit with a lower rate of convergence (Figure 6). Before 2005, the carbon intensity of the Yellow River Basin’s sub-basins followed the order “midstream > upstream > downstream”, shifting to “midstream > downstream > upstream” after 2005 in terms of absolute values. Regarding the rate of decrease, the average annual rate of decrease in the Yellow River Basin during the sample period was “upstream > midstream > downstream”, which was −3.44%, −1.60%, and −1.54%, respectively. This reveals distinct variations in the carbon reduction processes within the Yellow River Basin. Specifically, the carbon intensity in upstream of the Yellow River outperforms that in the midstream and downstream in both absolute values and decline rates, while the carbon intensity in the midstream significantly surpasses that in the upstream and downstream in absolute values.
3.2. Comparative Analysis of Carbon Intensity in Two Major Watersheds Based on the Theil Index
The preceding article analyzed carbon intensity variations across upper, middle, and lower reaches of two major river basins, focusing on absolute values, carbon reduction rates, and the reduction process. However, it did not quantitatively assess inter-basin and intra-basin disparities between the two. To address this gap, our study introduces the Terre index of carbon intensity using Equations (1)–(3) and conducted a comprehensive analysis of the inter-basin and intra-basin differences.
The carbon intensity Theil index reveals a substantial difference between the Yellow River Basin and the Yangtze River Economic Zone (Figure 7). Specifically, the Yangtze River Economic Belt exhibits a narrower range of 0.0326 to 0.0706 for the carbon intensity Theil index, with a mean value of 0.0482 and a standard deviation of 0.0105. In contrast, the Yellow River Basin displays a wider range of 0.0957 to 0.2976, a mean value of 0.1699, and a standard deviation of 0.0596. These findings underscore a significantly greater disparity in carbon intensity within the Yellow River Basin compared to the Yangtze River Economic Belt.
The carbon intensity Theil index for the Yangtze River Economic Zone exhibits a pattern of “steady fluctuation and subsequent steady decline”. In the period from 2000 to 2010, the index experienced steady fluctuations, ranging from 0.0513 to 0.0716, peaking at 0.0716 in 2003, marking the highest value in the last two decades. Analysis of the carbon intensity Theil index decomposition table (Table 2) reveals that during this period, differences in carbon intensity within the Yangtze River Economic Belt were influenced by both inter-basin (average contribution rate of 45.61%) and intra-basin differences (average contribution rate of 54.39%), primarily driven by inter-provincial variations in carbon intensity in the upstream basins. Moving on to the period from 2011 to 2020, the carbon intensity Theil index for the Yangtze River Economic Belt shows a stable decline. From 2011 to 2020, the index steadily decreased from 0.0451 to 0.0326, reflecting an average annual decline of −3.56%. In this timeframe, differences in carbon intensity within the Yangtze River Economic Belt were predominantly influenced by intra-basin disparities, accounting for an average contribution rate of 78.86%. Notably, the highest contribution to intra-basin differences came from inter-provincial variations in carbon intensity in the upper reaches of the Yangtze River. This indicates a diminishing trend in carbon intensity differences within the Yangtze River Economic Belt, primarily driven by intra-basin distinctions, especially the inter-provincial differences in the upper reaches of the Yangtze River.
The carbon intensity Theil index for the Yellow River Basin exhibits a pattern of “fluctuating decline initially, followed by a steady rise”. From 2000 to 2008, the index experienced a “fluctuating decline” phase, increasing slightly from 0.1933 in 2000 to 0.2245 in 2002 and then decreasing to 0.0957 in 2008, with an average annual decrease of −6.19%. In contrast, from 2009 to 2020, the index demonstrated a period of steady increase, rising from 0.0968 in 2009 to 0.2976 in 2020, with an average annual increase of 10.75%. Analysis of the carbon intensity Theil index decomposition table (Table 2) reveals that carbon intensity differences in the Yellow River Basin from 2000 to 2020 stem from both inter-basin differences (average contribution rate of 52.74%) and intra-basin differences (average contribution rate of 47.26%). Intra-basin differences indicate a hierarchy of “upstream basin contribution rate > midstream basin contribution rate > downstream basin contribution rate”. The contribution rate of inter-provincial carbon intensity differences in the upstream basin continues to rise, while the contribution rate of inter-provincial carbon intensity differences in the midstream basin decreases annually. The expansion of carbon intensity differences in the Yellow River Basin after 2009 is attributed to both inter-basin and intra-basin distinctions. Inter-basin differences are primarily caused by the high carbon intensity value in the middle reaches of the Yellow River, while intra-basin differences are predominantly driven by inter-provincial variations in the upper reaches of the Yellow River.
Comparatively speaking, the carbon intensity disparity between the Yangtze and Yellow River Basins follows a pattern of “one low, one high” and “one shrinking, one expanding”. Specifically, carbon intensity within the Yangtze River Economic Belt is decreasing and comparatively low, while within the Yellow River Basin, it is increasing and notably high. In the Yangtze River Economic Belt, variations in carbon emissions primarily stem from intrabasin distinctions, particularly noticeable in the upper reaches of the Yangtze River, largely attributable to Guizhou’s elevated carbon intensity. Positioned as the sole billion-ton coal base in Southwest China, Guizhou holds a pivotal role in the regional coal market, hence confronting the challenge of elevated carbon intensity. Urgent measures are required to mitigate environmental strain caused by substantial carbon emissions through the efficient utilization of fossil energy resources. In contrast, carbon intensity in the Yellow River Basin is influenced by both inter- and intrabasin differences. The middling region of the Yellow River exhibits heightened carbon intensity, while the upper reaches exhibit significant disparity, primarily due to the high carbon intensity prevalent in Shanxi and Ningxia provinces. Shanxi, a prominent coal-producing province, contributes approximately a quarter of China’s total coal output, driving local economic growth predominantly reliant on coal resources, consequently resulting in substantial carbon emissions, surpassing those of midstream provinces of the Yellow River. Conversely, Ningxia’s carbon emissions are primarily industrial, notably from chemical, iron and steel, non-ferrous, and building material sectors, yielding substantial carbon emissions. However, the homogeneity in industry development impedes the formation of core competitiveness to sustain local economic growth, contributing to high carbon intensity. The lack of diversified industry development further hampers the formation of core competitiveness, collectively elevating Ningxia’s carbon intensity beyond that of the Yellow River’s upper reaches.
4. Comparative Analysis of Factors Influencing Carbon Intensity in Two Major Watersheds
4.1. Selection of Carbon Intensity Impact Factors
Building upon Ehrlich’s (1971) IPAT (environmental load–population–affluence–technology) theory [32] and Dietz et al.’s (1994) extended STIRPAT model [33], as well as localized adaptations by Chinese scholars [16,18,23,34], this study identifies carbon intensity influencing factors within two major river basins (Table 3). Utilizing the natural break method, the factors—population size, economic development, technological inputs, energy structure, industrial structure, and government regulation—are hierarchically organized. The geodetector was employed to quantify the explanatory power of these factors on carbon intensity. Results indicate that all six influencing factor indicators passed the significance test (p ≤ 0.01), signifying that population, economy, technology, energy, industry, and government factors exhibit a significant explanatory influence on carbon intensity.
4.2. Comparison of Factor Detection Results
The order of influence of the six influential factors of carbon intensity differed between the two basins (Table 4, Figure 8). The q-values of the indicators in the Yangtze River Economic Belt, in descending order, were government regulation (0.8761) > population size (0.7856) > economic development (0.7850) > energy structure (0.7430) > industrial structure (0.7310) > technological input (0.5333). The q-values of the indicators in the Yellow River Basin, in descending order, are population size (0.8937) > energy structure (0.8796) > government regulation (0.8249) > economic development (0.6930) > industrial structure (0.6872) > technological input (0.6718).
Government regulation, population size, and economic development exert significant influence on the carbon intensity of the Yangtze River Economic Belt, with impact coefficients surpassing 0.75. Notably, government regulation holds the highest impact, registering a value of 0.9761. This dominance is attributed to the comprehensive “1 + N” policy system established by the government in guiding the “dual-carbon” policy within the economic belt. At the regional level, the Yangtze River Economic Belt has strategically implemented two Peak Carbon Action Programs—the Peak Carbon Implementation Program for the Yangtze River Delta Eco-Green Integrated Development Demonstration Zone and the Carbon Neutral Joint Action Program for Chengdu–Chongqing Twin Cities Economic Circle. These initiatives underscore the influential role of government regulations in addressing carbon emissions at the regional policy level, shaping the pathway toward achieving peak carbon in the Yangtze River Economic Belt. At the provincial level, Zhejiang, Chongqing, Shanghai, Jiangxi, Yunnan, Anhui, Hunan, Guizhou, Jiangsu, Hubei, and Sichuan have successively issued peak carbon implementation programs and implementation opinions. The varying strengths of policy controls among these provinces impact the degree of carbon intensity differences in the Yangtze River Economic Belt. Population size and economic development follow closely in influence, with respective impact coefficients of 0.7856 and 0.7850. This can be attributed to the presence of three major urban agglomerations—Yangtze River Delta Urban Agglomeration, City Agglomeration in the Middle Reaches of the Yangtze River, and Chengdu–Chongqing Twin Cities Economic Circle—serving as vital engines for economic development across the eastern, central, and western regions. These agglomerations collectively drive the socioeconomic development of the Yangtze River Economic Belt and contribute significantly to the nation’s economic output. The concentration of population and economic activities within these clusters enhances economic growth, subsequently reducing the carbon intensity of the Yangtze River Economic Belt. Disparities in economic volumes among the three major city clusters directly influence variations in carbon intensity within the economic belt.
Population size, energy structure, and government regulation exert significant influence on carbon intensity in the Yellow River Basin, with impact coefficients exceeding 0.8. Notably, population size has the highest impact on carbon intensity, followed by energy structure, with respective values of 0.8937 and 0.8796. Despite the Yellow River Basin’s population accounting for only 24.01% of the national total, its energy consumption represents a substantial 41.09% of the country’s total. This disproportion is attributed to the basin’s rich coal, petroleum, and natural gas resources, establishing it as a vital energy, chemical, and basic industrial base in China. However, the dominance of fossil energy in its energy consumption structure poses significant challenges for carbon emission reduction. Disparities in energy endowment, industrial status, and energy consumption structure among the provinces along the Yellow River Basin directly contribute to variations in carbon intensity levels. Government regulation holds the third-highest level of influence, at 0.8239. The absence of a regional-level basin carbon reduction policy in the Yellow River Basin is notable, with current policy guidance primarily governed by the “Outline of the Yellow River Basin Ecological Protection and High-Quality Development Plan” released in 2021. This reflects the government’s growing focus on fostering economic linkages among provinces and municipalities along the Yellow River, guiding collaborative efforts for ecological protection and high-quality development. At the provincial level, Shaanxi, Ningxia, Inner Mongolia, Qinghai, Shandong, Shanxi, Henan, and Gansu have successively implemented carbon peak programs, and policy strengths among these provinces significantly impact differences in carbon intensity within the Yellow River Basin.
In summary, there are both similarities and differences in the factors influencing variations in carbon intensity between the two basins. Population size and government regulations exert significant impact on carbon intensity in both basins, likely due to carbon emissions primarily originating from human activities and the inherently public nature of the carbon emission issue, influenced by policies. Notably, economic development has a more pronounced effect on carbon intensity in the Yangtze River Economic Zone, whereas energy structure plays a more crucial role in the Yellow River Basin. These distinctions emphasize the need to establish tailored carbon reduction paths based on local conditions in each basin.
4.3. Comparison of Interaction Detection Results
Additionally, the impact factors’ two-by-two interactions on carbon intensity were analyzed using geodetector-based interaction detection. This assessment aimed to determine whether the joint action of two factors would amplify or diminish the explanatory power of carbon intensity or if the two factors independently affected carbon intensity [28].
The overall interaction between impact factors is robust, with the explanatory power of factors post-interaction consistently showing two-factor enhancement (Table 5). The interaction values exceeding 0.9 indicate heightened synergy among the factors, emphasizing that no factor operates in isolation. The collective interaction better elucidates carbon intensity development. Therefore, in carbon emission reduction efforts, careful attention should be given to understanding the interaction and synergy among impact factors.
Internally, the interacting power of influencing factors varies. The identification of the top five interacting factors reveals that, among interactions influencing carbon intensity in the two basins, population size, energy structure, and government regulation are most frequently involved. This highlights the substantial interaction effects of these key factors on the carbon intensity differences between the two basins. Notably, economic development gains increased explanatory power for carbon intensity differences in the Yangtze River Basin after interacting with other factors, while industrial structure exhibits heightened explanatory power for carbon intensity differences in the Yellow River Basin post-interaction. Consequently, both major river basins should focus on understanding the effects of economic development and industrial structure on carbon intensity within their respective regions.
5. Conclusions
To aid China’s major river basins and provinces along the route in formulating sustainable emission reduction policies, this study compared carbon reduction processes and explored the impact of socioeconomic factors on carbon emission intensity in two major river basins. The conclusions are as follows:
Firstly, the carbon intensity disparity between the Yellow River Basin and the Yangtze River Economic Belt is pronounced. The Yellow River Basin exhibits significantly higher carbon intensity than the Yangtze River Economic Belt, with a slower convergence rate between the two. In the Yangtze River Economic Belt, intra-basin differences, particularly in the upper reaches, contribute to the disparity, while in the Yellow River Basin, both inter-basin and intra-basin variations play a role. These differences primarily stem from disparities in carbon intensity among provinces.
Secondly, there are both similarities and differences in factors affecting carbon intensity in the two watersheds. The commonality lies in population size and government regulation acting as shared drivers of carbon intensity in the two major basins, with influence coefficients exceeding 0.78. Conversely, an observed distinction is the greater influence of economic development on carbon intensity in the Yangtze River Economic Belt, with an impact coefficient of 0.7850. In contrast, energy structure exhibits a more pronounced impact on carbon intensity in the Yellow River Basin, with an impact coefficient of 0.8796.
Thirdly, interactions between factors affect carbon intensity similarly yet differently in the two watersheds. Synergies among population size, energy structure, and government regulation foster carbon reduction, with an interaction value exceeding 0.98. The disparity lies in the strong explanatory power of the interaction between economic development, industrial structure, and other factors on the Yangtze River Economic Belt and the Yellow River Basin, respectively.
6. Discussion
This study investigates the differences in carbon intensity between the Yangtze River Economic Belt and the Yellow River Basin from 2000 to 2020 and the influencing factors. Results suggest the former outperforms in carbon reduction, likely achieving “carbon peaking” and “neutrality” goals earlier. In the future, the two river basins should complement each other’s strengths and accelerate the process of carbon reduction.
These findings underscore the pivotal role of the two major watersheds in promoting coordinated development. Firstly, by leveraging the benefits of population agglomeration and its interaction with other factors, such as advocating for a low-carbon lifestyle to mitigate the adverse effects of population concentration on carbon emissions. Additionally, harnessing the effect of population size can drive progress in carbon reduction technology through investment in human capital, thereby enhancing energy efficiency. The Yangtze River Economic Belt should capitalize on the synergy between population size and economic growth, fostering innovation-driven economic expansion for continuous reduction in carbon intensity. Meanwhile, the Yellow River Basin should concentrate on the interaction between population size and energy structure, facilitating energy transformation, improving efficiency, and reducing carbon intensity. Secondly, government intervention is imperative, guiding policies and interacting with other factors. This involves refining carbon emission control policies, prioritizing dynamic balance and comprehensive planning in the economic development and “dual-carbon” promotion of both river basins. Furthermore, tailored and categorized policies should be implemented considering differentiation in carbon intensity and influencing factors within and between watersheds. The Yangtze River Economic Belt should enhance the overall and collaborative nature of policies, while the Yellow River Basin should establish a robust policy framework, transitioning from energy to carbon intensity control.
Additionally, the Yangtze River Economic Belt must utilize both market mechanisms and governmental interventions to address regional disparities in carbon intensity. Implementing the city cluster strategy is crucial for fostering high-quality economic development in less developed regions. Simultaneously, regulatory measures should be enacted to achieve the carbon peak and neutrality, dismantling policy barriers and integrating resources across different regions. Similarly, the Yellow River Basin must coordinate between overarching and local interests to propel the transition to low-carbon energy and economic growth, optimizing carbon reduction strategies at macro and micro levels. Tailored strategies are essential for different reaches of the Yellow River, considering their unique resource endowments, to effectively allocate carbon reduction targets, incentivize industries, and phase out traditional energy sources while preserving energy security and economic stability.
Finally, future research should address the following issues. Firstly, examining the influence of various factors on carbon intensity at smaller scales, including urban and county levels. Secondly, investigating spatial spillover effects of carbon emissions. Lastly, understanding how provinces with rapid carbon reduction can influence those with slower reduction processes, especially in the context of coordinated emission reduction policies in river basins.
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