Research concerning the green low-carbon economy and energy efficiency In 1995, the World Energy Council defined energy efficiency as the reduction of energy inputs required to provide equivalent energy services (Hui and Jie, 2022). The concept of a ‘low-carbon economy’ was first introduced by the UK government in its 2003 Energy White Paper, whose core tenets involve reducing energy consumption and carbon dioxide emissions through technological innovation, policy adjustments, and industrial restructuring under the guidance of sustainable development principles, thereby achieving a win-win outcome for economic growth and environmental protection. China’s development of ‘new quality productive forces’ was first proposed by General Secretary Xiinping in 2023. Its core lies in productivity generated by science and technology innovation, achieving breakthroughs in key disruptive technologies, thereby transcending traditional productive forces (Du, 2024). This aligns seamlessly with the development concepts of energy efficiency and the green low-carbon economy. On one hand, China’s green and low-carbon economic development must adhere to the fundamental principle and overarching orientation of green development, embedding green and low-carbon requirements throughout the entire process and all aspects of high-quality development (Hui and Jie, 2022). On the other hand, enhancing energy efficiency entails not merely reducing energy consumption but achieving sustainable economic, social, and environmental development through optimised energy utilisation. Within energy production, the green low-carbon economy propels the transformation and upgrading (Sun and Min, 2024) of conventional energy sources, bolstering the resilience of fossil fuels while elevating standards for their clean extraction and utilisation. In energy consumption, it drives energy-saving and carbon-reduction retrofits, advancing breakthroughs in emerging frontier technologies for decarbonisation and emissions reduction (Yiang and Wu, 2025). The seamless integration and dual-engine propulsion of new energy and digital technologies constitute the defining characteristic of this new wave of technological revolution and industrial transformation (Lin and Yuqiang, 2024). Concurrently, empirical research on green productivity within the green and low-carbon economy demonstrates that its evolution can be decomposed into several key drivers: changes in technical efficiency (reflecting catch-up effects from management optimisation and institutional improvements), technological progress (reflecting outward shifts in the frontier of green technologies), and changes in scale efficiency (reflecting adjustments towards optimal production scales). This decomposition aids in identifying the primary sources and constraints of green productivity growth in Chinese cities (Lin and Yuqiang, 2024).

Energy efficiency constitutes a pivotal factor in green and low-carbon economic development, with enhancing energy efficiency being crucial for reducing carbon emissions and achieving low-carbon economic growth. Liu and Wang, (2022) employed the LMDI full decomposition model to examine the decarbonisation factors within China’s manufacturing sector from 2000 to 2018, demonstrating that the energy intensity effect made a particularly significant contribution to carbon emissions within this industry. Concurrently, this paper examines energy efficiency from the perspectives of power generation and coal consumption. Research by Dongie and Qiaoianqiang (2022) indicates that under the dual-carbon framework, coal-fired power is transitioning from a primary power source to a foundational safeguard. Next-generation coal-fired technologies focus on three key directions: clean utilisation, efficient power generation, and low-carbon emissions. Among these, high-parameter, large-capacity units leverage system integration advantages to significantly enhance thermal efficiency and fuel adaptability, thereby robustly supporting the construction of a green energy efficiency system (Dong and Qiao, 2022). Tang Zhenyin (2023), (Tang, 2021), usingilin Province as a case study, employed a CGE model to analyse the impact of low-carbon development policies on the energy economy. The findings indicate that low-carbon policies have promoted the optimisation of the energy structure and the advancement of new energy technologies.

From 2005 to 2014, the spatiotemporal pattern of urbanisation efficiency within the Yangtze River Economic Belt (YREB) exhibited significant growth and regional disparities. Research by Jin et al. (2018) indicates that overall urbanisation efficiency rose from 0.34 to 0.53 during this period, representing a cumulative growth rate of approximately 54.07%. This assessment, based on input-output data from 110 cities analysed using Stochastic Frontier Analysis (SFA), revealed the trend of improving urbanisation efficiency and its spatial correlation characteristics within the region. Conversely, (Guo and Han, 2024) proposed a Generalised Luenberger Productivity Index (GLPI) employing distance elasticity shares as input weights to measure China’s urban green total factor productivity (GTFP) from 2000 to 2019, subsequently applying SFA for parametric decomposition. Findings indicate that China’s urban GTFP grew cumulatively by 140.59% during this period, primarily driven by technological progress (TC), while technological efficiency change (TEC) and scale efficiency change (SEC) exhibited significant spatio-temporal heterogeneity. Theoretical validation and empirical data matching confirmed the scientific rigour and applicability of this indicator. Additionally, (Zhu et al., 2020) employed principal component analysis to examine the spatiotemporal patterns of China’s energy efficiency from 2000 to 2018. They noted that energy efficiency levels across Chinese regions remain generally low, with the nation’s overall energy efficiency development characterised as moderately efficient. Guan et al. (2020) employed the SBM model to measure energy efficiency in the Yellow River Basin from 1979 to 2017, revealing significant spatial disparities in energy efficiency that transitioned from non-equilibrium to equilibrium. Chen and Zhang (2023) employed methods such as standard deviation ellipse analysis to study China’s agricultural energy efficiency over the past 2 decades. They concluded that spatially, China’s agricultural energy efficiency exhibits a stepped distribution pattern with higher efficiency in the east and lower in the west. Temporally, no clear convergence or divergence trend in efficiency distribution was observed.

Regarding factors affecting energy efficiency, (Liu and Lu, 2024) employed a DEA (Data Envelopment Analysis) game-based cross-efficiency model to evaluate China’s energy efficiency from 2012 to 2021. Building upon this, they constructed a Spatial Durbin Model (SDM) to analyse influencing factors. Findings indicate that technological progress and industrial structure exert positive impacts on China’s energy efficiency, whereas urbanisation levels and energy consumption structure exert negative impacts, with all effects being statistically significant. Concurrently, (Färe et al., 2026) extended Prieto and Zofío’s (2007) activity analysis model by incorporating non-freely disposable adverse outputs into the analytical framework. Using Danish input-output table data from 1990 to 2007, the study compared the differences between the aggregate joint production model in Data Envelopment Analysis (DEA) and the input-output model in measuring productivity changes, providing a methodological comparison for efficiency assessment where both desired and undesired outputs coexist. Furthermore, (Hu and Liu, 2011) demonstrated that investment levels, industrial structure, and energy consumption patterns are the primary factors significantly influencing regional disparities in energy efficiency. Suo and Yang (2025) analysed energy development in 11 western provinces from 2010 to 2021 using a three-stage DEA model, finding that levels of openness and shared development significantly promote energy efficiency improvements. Furthermore, scholars have indicated that relative energy prices (Yang et al., 2011), government policies, and digital financial development all exert certain influences on energy efficiency progress (Liu and Tian, 2019; Zhang and Shaofang, 2022).

In summary, existing research has yielded substantial findings in the respective domains of green low-carbon economies and energy efficiency. However, a review reveals that most studies focus on national, provincial, or regional scales, with limited analysis at the city level. Analytical paradigms and methodologies tend to be rather uniform, with few innovations in multi-model approaches. Furthermore, research linking these two areas remains scarce, and no unified perspective or consensus has yet emerged. Consequently, there remains scope for improvement and potential for marginal discussion in exploring these two concepts. Building upon existing research, this paper undertakes a systematic analysis of the spatiotemporal evolution of energy efficiency and its influencing factors, aiming to provide a valuable supplement and refinement to the current body of research.

This study, grounded in the context of green and low-carbon economic development, employs scientifically selected indicators to construct factors. Specifically, energy efficiency indicators are systematically evaluated from both input and output dimensions (Zhang and Shi, 2021). Simultaneously, building upon validated prior research, the most representative secondary indicators were purposefully selected for corresponding empirical data analysis. Regarding input indicators, the metric of urban employment per unit reflects human resources’ contribution to energy production. Drawing from Solow’s economic growth model, labour constitutes a core factor in the production function, exhibiting a non-linear relationship with energy efficiency. An appropriate labour scale can stimulate technological spillovers, thereby optimising energy efficiency (Solow, 1956). Furthermore, the aforementioned research indicates (Zhang and Shi) that labour scale influences energy utilisation efficiency improvements to a certain extent. Regarding national fiscal education expenditure, this indicator measures human capital investment. According to human capital theory, educational investment can indirectly optimise energy utilisation efficiency by enhancing workers’ skill levels (Schultz, 1961). Secondly, the year-on-year growth rate of fixed asset investment represents a key factor in green infrastructure investment for low-carbon transition (Zhong and Wang, 2022). Particularly within a low-carbon economic framework, green infrastructure investment (such as smart grid development) holds significant importance for low-carbon transition, effectively enhancing energy utilisation efficiency. The total energy consumption indicator directly reflects the scale of energy usage, while energy intensity per unit of GDP serves as a core monitoring metric for low-carbon policies, providing an intuitive representation of energy efficiency (Chu, 2009). The relationship between total energy consumption and economic growth constitutes a focal point in energy economics research (Liu et al., 2019a), with its trends offering crucial reference value for evaluating energy efficiency. Electricity consumption reflects energy structure issues (Xia and Wang, 2018). As a relatively clean energy source, shifts in electricity consumption reveal the extent of energy structure adjustment and optimisation, thereby influencing energy efficiency. Finally, research indicates that excessively high coal consumption inhibits low-carbon productivity (Liu et al., 2019b). Consequently, within a low-carbon economy, reducing coal consumption and increasing the proportion of clean energy represent key pathways to enhancing energy efficiency and achieving low-carbon development.

In terms of output dimensions, the regional gross domestic product (GDP) indicator serves as the core metric for measuring economic output. Energy productivity (GDP/energy consumption) directly quantifies the efficiency with which energy is converted into economic value (Zhang and Shi, 2021). By analysing the relationship between GDP and energy consumption, the economic efficiency of energy utilisation can be assessed, thereby providing a basis for formulating energy policies. Secondly, electricity generation capacity reflects the efficiency of energy supply output within a low-carbon economy context (Xia and Wang, 2018). Meanwhile, industrial SO₂ emissions serve as an indicator of the pollution costs associated with energy use. According to decoupling theory (OECD, 2002), promoting the decoupling of economic growth from pollutant emissions is essential. Reducing industrial SO₂ emissions is therefore crucial for lowering the environmental costs of energy utilisation and enhancing energy efficiency. Finally, industrial wastewater discharge indirectly reflects the environmental externalities of the energy industry (Camilo, 1997). By internalising negative environmental outputs as efficiency evaluation costs, the true benefits of energy utilisation can be assessed more comprehensively, driving energy consumption towards green and low-carbon development.

Based on the aforementioned theoretical analysis and adhering to fundamental principles of scientific rigour, comprehensiveness, effectiveness, and operational feasibility in indicator establishment, the energy efficiency system level (see Table 1) employs a guideline-level indicator system measured across input and output dimensions. Input indicators include: the guideline-level indicator system measures performance across input and output dimensions. Input indicators comprise: urban employment per unit area, year-on-year growth in fixed-asset investment, state-funded education expenditure, total energy consumption, electricity consumption, and coal consumption. Output indicators consist of regional GDP, electricity generation, industrial SO₂ emissions, and industrial wastewater discharge. Collectively, these form a comprehensive energy efficiency assessment framework.

This study focuses on 285 prefecture-level cities in China from 2012 to 2022 (excluding Tibet, Xinjiang, and Hong Kong, Macao, and Taiwan). The data primarily comes from the China Statistical Yearbook, the China Urban Statistical Yearbook, the China Energy Statistical Yearbook, the China Electricity Yearbook, and the statistical yearbooks of various prefecture-level cities. In terms of outlier handling, for observations that significantly deviate from the normal range, this study primarily employs outlier removal methods. Regarding missing value handling, given the absence of data for certain indicators in 2022 and minor missing data in historical years (with a missing rate below 5%), this study uses linear interpolation to supplement the missing data.

Using the non-expected output SBM model to measure energy efficiency across 285 prefecture-level cities yielded Malquist indices presented at provincial level (due to space constraints, SBM Malquist results are shown in Supplementary Appendix Table 2). These results illustrate the evolution of energy efficiency from 2013 to 2022 at both the national level and across eight major regions (see Figure 1). Overall, energy efficiency across all regions remained at a relatively high level throughout the study period, exhibiting a trend of ‘steady growth’. The energy efficiency growth rate in 2013 compared to the previous year was 1.137, and it has maintained a growth rate greater than 1 since 2013, gradually increasing. The fastest growth in energy efficiency measurement occurred in 2021, with a growth rate of 1.184. Since 2013, China’s energy efficiency has grown rapidly, benefiting from multiple positive factors. Firstly, the implementation of national energy-saving and carbon-reduction policies alongside dual-control measures for energy consumption has effectively driven industrial restructuring and optimisation, fostering the development of energy-saving and carbon-reduction technologies and industries. Secondly, technological progress has played a pivotal role in enhancing energy utilisation efficiency, particularly through the advancement of new energy sectors such as solar and wind power, which have significantly boosted energy efficiency. Furthermore, the optimisation of the energy consumption structure, notably the decline in coal’s share of consumption, has also contributed positively to energy efficiency gains. The elevation of economic development levels and household consumption standards has guided shifts in energy utilisation patterns, thereby reducing energy intensity. Policy guidance has also played a vital role in adjusting the energy mix and enhancing energy usage efficiency. Concurrently, improvements in China’s energy supply-demand balance and the vigorous development of clean energy, particularly the expansion of non-fossil fuel power generation capacity, have further contributed to energy efficiency gains. The combined effect of these factors has enabled China’s energy efficiency to achieve steady growth.

Analysed by region, China’s eight major areas exhibit an overall upward trend in energy efficiency (see Figure 4), though significant disparities exist between regions. Generally, coastal regions demonstrate higher energy efficiency growth rates than riverine regions, which in turn exceed those of inland areas. The eastern coastal and northern coastal regions recorded the most rapid growth in energy efficiency. The energy efficiency growth rate in the Northwest fluctuated around 1% between 2013 and 2020, before accelerating rapidly post-2021 in line with other regions. This indicates that the eastern and northern coastal regions benefit from higher levels of regional economic development, technological sophistication, and prioritisation of energy efficiency. Their geographical advantages have enabled them to prosper earlier than inland areas, leading to significantly higher economic development. with greater emphasis on ecological conservation and energy efficiency utilisation, alongside more mature technologies. Following the 18th CPC National Congress in 2012, which positioned ecological civilisation as a vital component of the ‘Five-in-One’ overall layout, coastal regions have continuously driven inland development. The implementation of China’s Western Development Strategy and Central Region Rise Strategy has also spurred rapid energy efficiency growth in riverine and inland areas, with the most pronounced effects and significant growth rate shifts evident after 2020. Concurrently, the disparity in energy efficiency growth rates among the eight major regions has progressively narrowed since 2020, indicating a gradual reduction in regional variations across China.

Examining regional growth trajectories reveals the Northeast exhibits the most pronounced rate of change, followed by the Middle Yellow River region. Other areas also demonstrate corresponding fluctuations, suggesting these two regions are pursuing divergent approaches to enhancing energy efficiency and undertaking corresponding experiments. The most notable variations in energy efficiency rates are observed in Heilongjiang and Henan provinces.

As shown in Figure 2, both the low-carbon economy and energy efficiency exhibit an overall upward trend, albeit at differing rates. Compared to energy efficiency, the growth of the low-carbon economy has been more moderate. Energy efficiency reached its peak in 2016, experienced a dip in 2017, saw a significant improvement in 2018, and subsequently stabilised. This pattern likely reflects the positive impact of policy initiatives and technological advancements on energy efficiency during this period. Observing the Sankey diagram (see Figure 3), significant regional disparities exist in national energy efficiency and low-carbon economic development. Coastal regions in the east and south demonstrate particularly strong performance in both low-carbon economy and energy efficiency, likely benefiting from higher levels of economic development, advanced technology application, and more comprehensive policy support systems. In contrast, the Northwest and Southwest regions exhibit relatively weaker performance in low-carbon economic development and energy efficiency, potentially linked to their industrial structures, energy mixes, and levels of economic development. Combining these insights reveals two key findings: firstly, regional development imbalances constitute a significant constraint on advancing national low-carbon economic development and energy efficiency; secondly, policy support and technological innovation serve as pivotal drivers for enhancing both low-carbon economic development and energy efficiency.

To characterise regional disparities in energy efficiency across China and its eight major regions, along with their sources, the Dagum Gini coefficient and subgroup decomposition method were employed to calculate the overall variance, intra-group variance, inter-group variance, and sources of variance in China’s energy efficiency from 2013 to 2022.

The dynamic trend of the national overall Gini coefficient for energy efficiency is analysed (see Table 2; Figure 4). Regarding the evolution of disparities, the national overall Gini coefficient decreased from 0.164 in 2013 to 0.108 in 2022, indicating a substantial reduction in the overall disparity of energy efficiency nationwide. With the development of the internet, connections between China’s major cities have become increasingly close, and the overall disparity in energy efficiency between cities has narrowed year by year.

Regarding intra-group variation (see Table 3; Figure 5), the middle reaches of the Yellow River exhibited the highest intra-group energy efficiency variation (0.250), while the southern coastal region showed the lowest (0.066). The northwest and eastern coastal regions followed with Gini coefficients of 0.245 and 0.224 respectively, with the remaining four major regions displaying fluctuating patterns. Regarding trends, the Gini coefficients across the eight major regions exhibited similar patterns, showing a wavelike, modest downward trajectory. The northern coastal region experienced minimal fluctuation, with an average annual coefficient of 0.1283. In 2017, all eight regions demonstrated pronounced inequality, marked by a sharp rise followed by a steep decline. Since 2020, the coefficients have gradually decreased in a wavelike pattern.

Regarding inter-regional disparities (see Table 4), the southern coastal regions and the Great Northwest region, as well as the Great Northwest region and the southern coastal regions, exhibit significant differences in the inter-group Gini coefficient (0.099), frequently presenting a ‘single-peak-double-peak’ phenomenon. This was observed in 2013 (0.178) and 2017 (0.178). Southern Coastal Regions exhibited the most pronounced inter-group Gini coefficient disparity (0.099), frequently displaying a ‘unimodal-bimodal’ pattern. This reached peaks in 2013 (0.178) and 2017 (0.209), before experiencing sharp declines in 2014 and 2018 respectively. The smallest inter-group differences were observed between Northern Coastal Regions and Middle Yangtze River Regions and Eastern Coastal Regions and Southwestern Regions.

The Gini coefficients for the Eastern Coastal and Northern Coastal Regions and Eastern Coastal and Middle Yellow River Regions exhibited the most pronounced fluctuations in 2017, while the Northeast and Southern Coastal Regions and Northeast and Southwest Regions showed the smallest fluctuations during that year.

Considering Table 4; Figure 6 together, the super-efficiency contribution rate emerges as the primary determinant of energy efficiency, with an average contribution rate of 64.344%. Moreover, the variation between groups exceeds that within groups. From 2013 to 2022, the line depicting super-density exhibits significant fluctuations, yet the overall trend remains upward. This may indicate that this factor’s influence on energy efficiency has intensified during this period, potentially linked to global energy price fluctuations, the introduction of new technologies, or shifts in energy policy. For inter-group variations, this may relate to factors such as technological advancement, policy support, and resource allocation differences between groups. Intra-group analysis reveals a relatively stable trend with a slight downward trajectory, indicating that energy efficiency disparities among group members have not significantly widened. This suggests a convergence in energy efficiency performance, potentially stemming from improved mechanisms for sharing technical knowledge and the application of systematic management measures. Such approaches foster coordinated enhancement of group-level energy efficiency by reducing practical heterogeneity.

Based on the national energy efficiency kernel density map (see Figure 7), the national energy efficiency exhibits the following four characteristics.

Two distinct peaks are discernible in Figure 13. These peaks exhibit an increasing trend over time, with their positions shifting progressively to the right. This indicates an overall upward trajectory in national energy efficiency. The emergence of a peak at the far right suggests heightened national emphasis on energy efficiency development, signifying continuous improvement. The presence of two peaks may imply the existence of two distinct economic entities or sectors exhibiting divergent trends in energy efficiency. One may be maintaining its existing energy efficiency level while the other is improving;

Regional variations are primarily manifested in the following aspects:

All eight major regions nationwide exhibit multi-peak phenomena, with peak heights showing an overall upward trend. Notably, the main peaks of the kernel density curves narrow in the middle Yangtze River region (see Figure 8), the Eastern Coastal Region (see Figure 9), the Northwest Region (see Figure 10), and the Middle Yellow River Region (see Figure 11) exhibit narrowing widths of their principal peaks in the kernel density curves. Conversely, the Northern Coastal Region (see Figure 12) and Southern Coastal Region (see Figure 13) show widening principal peaks, while the Northeast Region (see Figure 14) and Southwest Region (see Figure 15) maintain largely unchanged principal peak widths.

This indicates a trend towards narrowing regional disparities in energy efficiency within the middle Yangtze River region, eastern coastal region, Northwest China region, and middle Yellow River region. whilst regional disparities in energy efficiency within the northern coastal and southern coastal regions are expanding. Regional differences in the northeast and southwest regions remain largely unchanged. The energy efficiency kernel density curves for the Northwest, Middle Yellow River, and eastern coastal regions exhibit a wave-like pattern transitioning from ‘uniform distribution–bimodal–unimodal’, with peaks distinctly shifting to the right. This indicates a marked improvement in energy efficiency across these areas. The energy efficiency kernel density curve for the southern coastal region exhibits a fluctuating pattern of ‘uniform distribution – narrow bimodal – broad bimodal’, accompanied by polarisation. This suggests that energy efficiency in the southern coastal region may be linked to urban development, with coastal cities potentially demonstrating higher energy efficiency.

Regarding extensibility, the energy efficiency and density curves for the Northeast, Eastern Coastal, Southwest, and Middle Yangtze regions exhibit pronounced right-tailing phenomena alongside broadening of the primary peaks. This indicates gradually widening regional disparities in energy efficiency across these areas. The northern coastal region’s energy efficiency kernel density curve exhibits an overall quadratic shape, indicating diminishing regional disparities with efficiency values converging around 1.3. The middle Yellow River region and the Greater Northwest region show kernel density curves that initially peak before gradually broadening and declining, suggesting that regional differences in energy efficiency within these areas may be narrowing due to policy interventions or other factors.

The southern coastal region exhibits a curve with a low central peak flanked by high values, indicating pronounced polarisation and significant intra-regional disparities. Polarisation is also present in the middle Yellow River region, the Northwest, the middle Yangtze region, and Southwest China, though these areas are stabilising over time with values highly clustered around 1.2, 1.1, 1.0, and 0.9 respectively. Notably, the middle Yellow River region showed high concentration in 2014 and 2019, 2016 and 2019, while exhibiting polarisation in 2015 and 2017, with this trend markedly diminishing after 2019.

Regarding volatility and stability, the energy efficiency kernel density curve for the Northeast region remained relatively stable overall, with the peak consistently at 1.2 and the width largely unchanged, indicating steady development in energy efficiency. In contrast, the energy efficiency kernel density curves for the other seven major regions exhibited significant variations, reflecting differing patterns of energy efficiency development across these areas.

Against the backdrop of low-carbon economic development, this study employs provincial and prefecture-level panel data from mainland China (excluding Tibet, Xinjiang, Hong Kong, Macao, and Taiwan) spanning 2012–2022. By comprehensively evaluating energy efficiency across multiple dimensions—including socioeconomic development, green ecological progress, and technological innovation capacity—the following conclusions are drawn:

First, employing the non-expected output SBM model to measure and analyse the green low-carbon economy and energy efficiency of China’s 285 prefecture-level cities (measurement results presented in Appendices 1 and 2), the findings indicate a positive trajectory in the growth rate of energy efficiency across Chinese regions. Fluctuations in this growth rate reveal the challenges and opportunities faced by different regions in their energy economic development processes.

Second, Dagum Gini coefficient analysis identified the sources of energy efficiency disparities. The national aggregate Gini coefficient decreased from 0.164 in 2013 to 0.108 in 2022, indicating a substantial reduction in overall regional energy efficiency differences. Intra-regional disparities, inter-regional disparities, and hyper-variability density disparities were identified, with hyper-variability density contributing most significantly to overall disparities (average contribution rate: 64.344%). Kernel density estimation revealed multi-modal distributions across all eight major regions, with overall peak height increases, indicating a national upward trend in energy efficiency and gradually diminishing inter-regional disparities. Markov chain analysis indicates that under the high-level lag type, the probability of transition from low to medium-low levels (37.94%) exceeds that under the low-level lag type (31.03%). Concurrently, under high-level lag conditions, the probabilities of upward transitions from low, medium-low, and medium-high levels to the next tier are 24.05%, 26.67%, and 21.74% respectively, reflecting a relationship of growth and decline. This indicates that the dynamic evolution of energy efficiency levels exhibits relative stability, albeit with inherent instability.

Thirdly, empirical analysis of aggregate data reveals a substantial narrowing of overall national energy efficiency disparities. Both inter-regional and intra-regional variations in energy efficiency are diminishing, suggesting gradual improvement in regional disparities within China’s energy efficiency landscape.

Firstly, grounded in the theory of coordinated regional development and the intrinsic requirements of green transition, empirical research by Pan et al. (2019) demonstrates that China’s green productivity index reflects balanced progress in green economic growth over recent decades. This indicates that systematic efforts can yield policy improvements conducive to low-carbon economic transformation (Pan et al., 2019). It is recommended to establish a differentiated policy support system, prioritising targeted assistance for China’s central-western regions and the old industrial bases in the northeast: on the one hand, by establishing specialised green and low-carbon development funds to focus on supporting regional infrastructure modernisation, clean technology innovation, and industrial transformation and upgrading, thereby fostering high-tech industrial clusters; Secondly, establishing an ‘East-Central/Western’ industrial synergy platform to refine industrial transfer coordination mechanisms and factor market allocation systems, thereby promoting cross-regional resource optimisation and complementary advantages to systematically enhance regional green and low-carbon development capabilities.

Secondly, it is recommended that the government establish an incentive policy framework for green technological innovation. Research by Wang et al. (2022) demonstrates that low-carbon pilot city policies can motivate enterprises to pursue green technological innovation, thereby achieving more efficient energy utilisation and emissions reduction (Wang et al., 2022). This further corroborates the synergistic effects of technological innovation and policy intervention in driving the transition towards a green, low-carbon economy. This should be achieved by increasing fiscal investment in science and technology, implementing tax incentives such as additional deductions for R&D expenditure, and establishing diversified science and technology financial support mechanisms. The focus should be on cultivating enterprises’ innovation capabilities in key areas including energy-saving and emission-reduction technologies, new energy development and utilisation, and resource recycling. Concurrently, the green transformation and upgrading of traditional industries should be accelerated. This involves enhancing industrial energy efficiency through intelligent technological upgrades, phasing out high-energy-consuming outdated production capacity in an orderly manner, and prioritising the cultivation of strategic emerging industries such as new energy, new materials, and high-end equipment manufacturing. Regarding energy structure adjustment, it is recommended to implement a diversified energy development strategy, systematically reducing the proportion of fossil fuel consumption. Technological innovation and infrastructure improvements should enhance the absorption capacity and supply stability of renewable energy sources such as wind, solar, and hydro power, thereby constructing a modern energy system that is clean, low-carbon, secure, and efficient.

Thirdly, establish a multi-tiered energy efficiency governance system. Chen et al. (2021); Z. Liu et al. (2021) examined China’s regional green governance policies, indicating that technological and environmental factors are key determinants of regional energy efficiency performance, with effective governance models narrowing inter-regional efficiency gaps (Chen et al., 2021). Similarly, Gabriela Araujo-Vizuete et al. (2025) proposed a hybrid governance model for Ecuador’s energy transition, balancing top-down and bottom-up approaches to foster social participation and enhance energy governance effectiveness (Araujo-Vizuete and Robalino-López, 2025). Consequently, the following recommendations are proposed for this paper: Firstly, establish a lifecycle-based energy management system. Implement mandatory energy efficiency standards and tiered energy conservation targets to guide comprehensive development towards energy-efficient production and consumption patterns. Secondly, adopt a low-carbon development paradigm in urban planning. Optimise urban form and structure using spatial econometric methods, prioritising resource-efficient cities, enhancing green transport networks, and promoting ultra-low energy building technologies. Finally, establish cross-regional energy coordination mechanisms, leveraging big data platforms to optimise energy infrastructure allocation. Through interregional energy complementarity and resource sharing, systematically enhance overall energy utilisation efficiency, ultimately constructing a multi-stakeholder governance framework for energy conservation and carbon reduction involving government, market, and society.

Fourthly, research by Wei and Ma (2024) demonstrates that green technological progress significantly enhances carbon reduction capabilities, with particularly pronounced effects when supported by active carbon markets, highlighting its pivotal role in achieving the dual carbon goals (Wei and Ma, 2024). Hu et al. (2023) regional research further reveals the multidimensional characteristics of green development: taking China’s Yangtze River Delta economic belt as an example, multidimensional economic development can positively enhance green technology development efficiency, with regional disparities gradually diminishing over time (Hu et al., 2023). Consequently, efforts should focus on establishing a comprehensive innovation system for green technology development: by increasing R&D intensity, deepening strategic collaborations with universities and research institutions, and establishing collaborative innovation mechanisms integrating industry, academia, research, and application to achieve breakthroughs in key energy-saving and emission-reduction technologies. Concurrently, systematic advancement of energy-efficient retrofits for production equipment should be pursued, incorporating intelligent energy management systems and advanced energy-saving process technologies. This will sustainably enhance green total factor productivity and micro-level energy utilisation efficiency, thereby providing a robust microfoundation for constructing a clean, low-carbon, secure and efficient modern energy system, and propelling the realisation of China’s sustainable energy development objectives.