Measuring Green Total Factor Productivity in China’s Power Industry Based on Super-Efficiency SBM and GML Index Models

Abstract

Against the backdrop of accelerating global energy transition, China, as the world’s largest energy producer and consumer, has a crucial impact on achieving carbon neutrality goals through the green development of its power industry. Green total factor productivity is an important indicator for measuring the green development of the power industry. Utilizing provincial panel data from 30 regions in China covering the period 2012–2023, using MATLAB R2021a software, this study firstly measures the static GTFP of China’s power industry using a Super-Efficiency Slack-Based Measure (SBM) model incorporating undesirable outputs. Subsequently, the dynamic GTFP is measured and analyzed using the Global Malmquist–Luenberger (GML) index model. The model decomposes GTFP change to examine the contributions of technical efficiency change and technological progress. The findings reveal that (1) the static GTFP of China’s provincial power industry is generally low, with significant regional disparities, with Jiangsu, Yunnan, Beijing, Zhejiang and Sichuan ranking among the top five nationally; (2) the average GTFPs in eastern and western China are higher than in the central region. Overall, the GTFP of China’s power industry exhibits an upward trend, which is primarily driven by technological progress. Based on these conclusions, the study proposes policy recommendations to enhance the power industry’s GTFP, which can offer theoretical insights for facilitating its green transition and sustainable development.

1. Introduction

Against the backdrop of accelerating global climate change and increasingly stringent resource constraints, the pursuit of green and low-carbon development has emerged as a defining trend of the new era. By 2024, global energy-related carbon emissions reached 37.8 billion tons, accounting for over 85% of the total global carbon emissions, which highlights the crucial role of energy system transformation in addressing the climate crisis and achieving sustainable development. As the world’s largest developing country, China has historically depended on an energy-intensive and extensive growth model, which has contributed to extraordinary economic achievement and rapid industrialization. However, it has also imposed heavy energy consumption, environmental degradation, and ecological stress. As a result, the country now faces unprecedented challenges in advancing toward a sustainable development pathway that reconciles economic growth with environmental protection. Within this context, the electric power industry occupies a particularly critical position. As both a cornerstone of the national economy and the single largest source of carbon emissions, its role in the broader green transition is decisive. The extent to which the electric power industry can achieve a comprehensive transformation toward sustainability will not only determine China’s ability to meet its carbon peak and carbon neutrality goals, but will also directly shape improvements in energy security, ecological well-being, and life quality for its citizens.

Green total factor productivity (GTFP), recognized as a long-term driver of economic growth [1], extends the traditional total factor productivity (TFP) framework by explicitly integrating energy consumption and environmental pollution. This extension enables a more comprehensive and objective assessment of the quality of sustainable development and the degree of green transition across provincial electric power industries. Consequently, the GTFP has emerged as a key indicator for evaluating the high-quality green development of the power sector. Improving the GTFP is thus indispensable for achieving the sustainable transformation of the power industry and carries profound implications for fostering its clean and low-carbon transition. In this regard, scientific measurement and systematic analysis of the GTFP in China’s power industry can play a crucial role in accurately identifying regional heterogeneity, informing the optimization of policy framework as well as resource allocation, and promoting technological innovation. Ultimately, such efforts provide robust theoretical foundation and practical decision-making reference for advancing the industry’s green and low-carbon transformation.

Existing studies have primarily adopted three methodological approaches to measure the GTFP. The first is the parametric approach represented by stochastic frontier analysis (SFA). For example, Cui et al. [2] employed a translog SFA model to estimate the agricultural GTFP of major grain-producing areas in the Songhua River, Yellow River, and Yangtze River basins under environmental constraints during 2004–2018. The second is the semi-parametric approach, mainly including the Olley–Pakes (OP) and Levinsohn–Petrin (LP) methods. The OP method, proposed by Olley and Pakes [3], estimates the TFP by addressing the simultaneity problem through the use of investment as a proxy variable. However, Levinsohn and Petrin argued that intermediate inputs serve as a more appropriate proxy, as they better account for productivity shocks associated with investment [4]. This refinement improves estimation accuracy, which is thus referred to as the LP method. The third is the non-parametric approach, represented by the DEA–Malmquist index and its extensions. For instance, Ni et al. [5] applied the DEA–Malmquist index to measure the GTFP of the Yangtze River Delta region. As an extension of the DEA framework, the slack-based measure (SBM) model has been widely adopted to evaluate the GTFP. For example, Dai et al. [6] combined the super-efficiency SBM model with the Malmquist index to assess both the static and dynamic GTFP of 30 Chinese provinces; Li and Chen [7] combined the ML index with the SBM model to obtain a new method for measuring the GTFP, namely the SBM-ML model; while Ma et al. [8] employed the SBM-GML model to estimate the GTFP of maize production across different regions.

Research on the green total factor productivity of power industry remains relatively limited. On the one hand, most studies in the industrial sector have focused on the overall industrial system rather than specific industries, with few addressing the power sector explicitly. For example, Qiu et al. [9] employed the Malmquist–Luenberger (ML) index to estimate industrial GTFP and examined the impact of environmental regulation, while Yao et al. [10] applied the Global Malmquist–Luenberger (GML) index to evaluate the industrial GTFP of 281 Chinese cities and investigated the influence of land-leasing strategies. On the other hand, existing studies on the power industry have primarily concentrated on environmental efficiency rather than the GTFP. For instance, Pan et al. [11] applied a super-efficiency SBM model combined with the ML index to evaluate the environmental efficiency of provincial power industries in China from 2008 to 2017, which highlighted the role of technological progress. Similarly, Dong et al. [12] constructed a multi-level meta-frontier BAM model that accounts for technological and regional heterogeneity to assess provincial power sector efficiency, while Yuan et al. [13] used an improved three-stage SBM-DEA model to estimate the total-factor carbon emission efficiency of listed thermal power companies from 2016 to 2022. Moreover, Wei et al. [14] utilized a three-stage DEA model with game cross-efficiency to measure the carbon reduction efficiency of provincial power industries and examined the spatial convergence effect. Yang and Chen [15] measured the GTFP of power generation enterprises as an example when studying the measurement of green total factor productivity growth.

In summary, previous studies have shown different methods for measuring the GTFP, such as the parametric method used in reference [2], the semi-parametric method used in references [3,4], and the non-parametric method used in references [5,6,7,8]. In recent years, the SBM-GML model has gradually been used by scholars to measure the GTFP. Based on the ordinary SBM-GML model, this paper selects the super- efficiency SBM-GML model containing unexpected outputs to measure the GTFP in the power industry, which can consider unexpected outputs such as pollution emissions and calculate decision units with efficiency values greater than 1, in order to compare the efficiency differences between different decision units. There have been some studies which have used DEA methods, including the SBM-GML model, to calculate the GTFP. However, there is almost no research focusing on the GTFP in the power industry. References [9,10] focus on the industrial industry, while references [11,12,13,14] study the environmental efficiency and carbon emission efficiency of the power industry. Reference [15] calculates the GTFP of power generation enterprises, but mainly studies the calculation method of the GTFP rather than conducting an in-depth analysis of the GTFP of power generation enterprises. However, the perspective of the GTFP of power generation enterprises is different from that of power industry. Therefore, this paper has a certain research value. Although prior studies have explored industrial GTFP and examined the efficiency of the power sector, research focusing specifically on the green total factor productivity of the power industry remains limited. In particular, insufficient attention has been devoted to its temporal and spatial heterogeneity. To address these research gaps, this study makes three primary contributions.

First, drawing on panel data from 30 provincial regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan) covering the period 2012–2023, we employ a super-efficiency SBM model incorporating undesirable outputs to measure the GTFP of power industry and to analyze its static characteristics. The super-efficiency SBM model can measure decision units with efficiency values greater than one and compare them between decision units, which can reflect the efficiency level more realistically.

Second, the GML index model is used to measure the growth rate of green total factor productivity in the power industry. Compared to the traditional ML index model, the GML index model has multiple frontiers, which can analyze the change in GTFP from a global perspective and decompose it into the change in technical efficiency and the change in technical progress. Therefore, it can reveal dynamic characteristic and driving mechanism of the GTFP, which can offer new insights into the industry’s green transition.

Third, based on the empirical results, we propose targeted policy recommendations to enhance the sustainable development of China’s electric power industry. These findings not only provide theoretical guidance but also assist policymakers and industry practitioners in better understanding the complexity of the industry’s green transformation, which can serve as a valuable reference for the optimization and adjustment of relevant low-carbon transition policies.

The overall research framework is presented in Figure 1. Section 2 introduces the model and research method, and the GTFP evaluation indicator system and data sources are also provided. An Empirical Analysis is conducted in Section 3, and the static GTFP and dynamic GTFP are analyzed in detail. Finally, the conclusions and recommendations are given in Section 4, which summarizes the main conclusions of this research and proposes related recommendations for promoting the sustainable development of the power industry in China.

2. Research Model and Data Sources

2.1. Research Methods and Model Construction

2.1.1. Super-Efficient SBM Model Incorporating Non-Intended Outputs

The slack-based measure (SBM) model, originally proposed by Tone in 2002, represents an important advancement of the conventional DEA framework by explicitly incorporating slack variables into the efficiency evaluation process. Unlike traditional models, which may overlook non-radial inefficiencies, the SBM framework is capable of capturing redundant inputs and undesirable outputs, thereby enabling a more comprehensive and precise assessment of the performance of decision-making units (DMUs) [16]. This refinement allows the model to account for the full range of potential improvements in both resource utilization and output generation, which is particularly valuable in sustainability-oriented studies where efficiency should be evaluated under multiple environmental and economic constraints. Building on this foundation, the super-efficiency SBM model further extends the analytical capacity of the framework by allowing efficiency scores greater than unity to be calculated. This enhancement not only facilitates more meaningful comparisons among efficient DMUs but also provides a more realistic and differentiated reflection of relative efficiency levels, which can offer stronger insights into best practices and frontier shifts within the system under study [17]. The mathematical formulation of the super-efficiency SBM model is presented as follows [18,19].

$$\begin{array}{l}\min \rho ={\displaystyle \frac{1+\frac{1}{m}{\displaystyle {\sum }_{i=1}^m\frac{s_i^{-}}{x_{ik}^t}}}{1-\frac{1}{s_1+s_2}\left({\displaystyle {\sum }_{r=1}^{s_1}\frac{s_r^{+}}{y_{rk}^t}+{\displaystyle {\sum }_{z=1}^{s_2}\frac{s_z^{b-}}{b_{zk}^t}}}\right)}}\\ s.t.\left\{\begin{array}{c}{\displaystyle {\sum }_{j=1,j\ne k}^n{x}_{ij}^t{\lambda }_j-s_i^{-}\le x_{ik}^t}\\ {\displaystyle {\sum }_{j=1,j\ne k}^n{y}_{rj}^t{\lambda }_j+s_r^{+}\ge y_{rk}^t}\\ {\displaystyle {\sum }_{j=1,j\ne k}^n{b}_{zj}^t{\lambda }_j-s_z^{b-}\le b_{zk}^t}\\ 1-{\displaystyle \frac{1}{s_1+s_2}}\left({\displaystyle {\sum }_{r=1}^{s_1}\frac{s_r^{+}}{y_{rk}^t}+{\displaystyle {\sum }_{z=1}^{s_2}\frac{s_z^{b-}}{b_{zk}^t}}}\right)>0\\ \lambda ,s^{-},s^{+}\ge 0\\ \begin{array}{l}i=1,2,\dots ,m;j=1,2,\dots ,n\\ r=1,2,\dots ,s_1;z=1,2,\dots ,s_2\end{array}\end{array}\right.\end{array}$$

(1)

where ρ represents the efficiency value; x, y, and b denote inputs, expected outputs, and unexpected outputs, respectively; i, r, and z denote the quantities corresponding to inputs and outputs, respectively; j denotes the number of DMUs; $s_i^{-}, s_r^{+}, s_z^{b-}$ denote input factor redundancy, expected output factor deficiency, and unexpected output factor redundancy, respectively; λ denotes the weighting variable.

The traditional DEA model is highly sensitive to changes in input and output data. Minor changes in data may lead to significant differences in results, which will result in the instability issue. Comparatively speaking, the super-efficient SBM model can simultaneously consider the slack variables of input and output, which can measure the inefficiency of the DMU in terms of input and output, thus enabling the model to more comprehensively evaluate the efficiency of the DMU. In addition, the super-efficient SBM model can better handle non-radial and non-proportional situations, which can provide a more accurate efficiency improvement direction for the DMUs.

2.1.2. GML Index Model

To provide a more systematic and comprehensive assessment of the green total factor productivity of power industry, this study adopts the GML index model to perform a dynamic evaluation of efficiency and productivity changes over time. The GML index can be further decomposed into two components, namely efficiency change (EC) and best practice change (BPC), which respectively capture variations in managerial performance and shifts in the technological frontier [20]. Efficiency change refers to the change in conversion efficiency between input and output in the production process at a given level of technology. It measures how the DMU can more effectively use its inputs to generate outputs, or reduce the use of inputs while keeping the output unchanged. Best practice change refers to the changes in production potential caused by factors such as technological innovation and improvements in the production method. It measures the movement of production technology boundaries; that is, the decrease in input required to produce the same amount of output or the increase in output that can be produced by producing the same amount of input at the same efficiency level. Compared with the conventional ML index, which relies on a period-specific DEA framework and constructs multiple local frontiers, the GML index is based on a global DEA approach which builds a unified production frontier encompassing all provinces and all years within the sample period. This global perspective ensures greater consistency and comparability across time and space, which can effectively address the infeasibility issues that often arise in the application of the traditional ML index. Consequently, the GML index provides a more robust analytical tool for evaluating the dynamic evolution of the GTFP in the power industry. Specifically, the index measures the growth rate of the GTFP, with values greater than one indicating improvement and values less than one signifying a decline. Similarly, an EC value greater than one reflects an increase in technical efficiency, whereas a BPC value greater than one denotes technological progress, with values below unity implying deterioration in these respective dimensions. The specific mathematical formulation of the GML index is presented as follows [21].

$$GM{L}^{t,t+!}\left(x^t,y^t,b^t,x^{t+1},y^{t+1},b^{t+1}\right)={\displaystyle \frac{1+D^G(x^t,y^t,b^t)}{1+D^G(x^{t+1},y^{t+1},b^{t+1})}}=E{C}^{t,t+1}\times BP{C}^{t,t+1}$$

(2)

$$E{C}^{t,t+1}={\displaystyle \frac{1+D^t(x^t,y^t,b^t)}{1+D^{t+1}(x^{t+1},y^{t+1},b^{t+1})}}$$

(3)

$$BP{C}^{t,t+1}=\left[{\displaystyle \frac{1+D^G(x^t,y^t,b^t)}{1+D^t(x^t,y^t,b^t)}}\times {\displaystyle \frac{1+D^{t+1}(x^{t+1},y^{t+1},b^{t+1})}{1+D^G(x^{t+1},y^{t+1},b^{t+1})}}\right]$$

(4)

where $D^G()$ denotes the global directional distance function; $D^t()$ represents the directional distance function at period t; $D^t(x^t,y^t,b^t)$ denotes the gap between the actual efficiency at period t and the current frontier.

2.2. Indicator Selection and Data Analysis

2.2.1. Indicator Selection and Data Sources

The measurement of green total factor productivity in China’s electric power industry relies on a carefully constructed system of input and output indicators which comprehensively reflect the industry’s production characteristics as well as its environmental impact.

The input indicators encompass three key dimensions: capital input, labor input, and resource input [22,23,24]. Specifically, the installed capacity is employed to capture capital investment and infrastructure accumulation in the power industry. The number of employees engaged in the electric power industry is used to represent labor input and workforce contributions, and coal consumption for power generation serves as the primary proxy for resource input, which reflects both the scale of fossil fuel dependency and the intensity of resource utilization. Installed capacity is chosen as a proxy variable for capital investment because it can directly measure the capital stock scale and technological equipment level of the power industry. It reflects the maximum potential production capacity of the industry at a specific point in time, which is a solid foundation for building green total factor productivity. Compared to using flow indicators such as investment amount, the stock indicator of installed capacity can more stably reflect the long-term investment status of capital factors, and the data at the provincial level is usually more complete, which can ensure the feasibility of the research. The use of the number of employees in the power industry to measure labor input is a widely adopted and data-accessible approach in related research. It intuitively reflects the scale of human resources required to maintain power production, operation, and maintenance. Although this indicator does not fully reflect the heterogeneity of workers in terms of educational background, skill level, etc., but as a fundamental indicator for measuring the quantity of labor input, it can effectively reflect the industry’s ability to absorb employment and basic labor costs. Calculating resource input by multiplying the power generation by the standard coal consumption is a very clever approach. It comprehensively reflects the total level of energy consumption in the process of electricity production. The power generation represents the output scale, and the standard coal consumption represents the energy input efficiency per unit of output. The product of the two can more accurately capture the true resource cost invested to achieve a certain output, which is the key to evaluating the comprehensive energy utilization efficiency and technological progress.

On the output side, the indicators are divided into desirable and undesirable categories to reflect the dual goals of economic performance and environmental sustainability. Electricity generation, as the core product of power industry, is designated as the desirable output indicator, which highlights the industry’s contribution to meeting energy demand and supporting economic growth. Conversely, the carbon dioxide emissions from the power industry are adopted as the undesirable output indicator, which signifies the negative externalities associated with fossil fuel-based electricity production and aligns with the broader emphasis on low-carbon development. In the context of the current global response to climate change and China’s vigorous promotion of the goals of “carbon peak and carbon neutrality”, the selection of CO₂ emissions as an unexpected output has strong historical significance and policy relevance. The power industry is the main source of carbon emissions, and controlling its CO₂ emissions is of the utmost importance when it comes to achieving carbon neutrality. This choice enables the measurement of green total factor productivity to more accurately reflect the effectiveness of the industry’s green and low-carbon transformation. Compared to local pollutants such as SO₂, the impact of CO₂ as a greenhouse gas is global, and incorporating it into unexpected outputs can better reflect the international perspective of research and attention to long-term environmental responsibility. This study uses the carbon emission factor method of the Intergovernmental Panel on Climate Change (IPCC) to account for carbon dioxide emissions in the power industry. Specifically, carbon emissions in the power industry come from the power generation multiplied by the corresponding carbon emission factor value from the IPCC; for details on calculation process, refer to literature [25]. Detailed sources of the data for all input and output indicators are systematically reported in

Table 1. Indicator system for measuring green total factor productivity in the power industry.

Variable Properties	Variable Type	Indicator	Data Source
Input indicators	Capital investment	Installed capacity	China Electricity Council
	Labor input	Number of employees in the power industry	China Labor Statistical Yearbook
	Resource allocation	Power generation coal consumption	China Electricity Council
Output indicators	Expected output	Electricity generation	China Electricity Council
	Non-expected output	CO2 emissions in the power industry	IPCC Emission Factor Method Calculation [25]

.

2.2.2. Descriptive Statistics

Considering data availability, this study selects panel data from 30 provincial regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan) for the period 2012–2023. Missing values for certain indicators are imputed using interpolation and the exponential smoothing (ETS) method. Descriptive statistics for all input and output indicators are presented in

Table 2. Descriptive statistical analysis results.

Indicator	Unit	Mean	Standard Deviation	Maximum Value	Minimum Value
Installed capacity	Ten thousand kilowatts	5992.052	3751.062	18,958.000	497.000
Number of employees in the power industry	Person	96,738.000	51,765.320	253,007.000	13,237.000
Power generation coal consumption	Ten thousand tons	6503.694	4165.363	17,650.000	610.000
Electricity generation	Hundred million kilowatt-hours	2214.088	1434.707	6306.000	211.000
CO2 emissions in the power industry	Ten thousand tons	16,096.593	10,309.262	43,683.948	1509.230

. The descriptive statistics for the indicators related to China’s power industry as shown in Table 2 provide a clear picture of the scale and variation of the power industry across different regions for the observed period. The following section analyzes each indicator in detail.

Installed capacity, which represents the maximum potential output of electricity generation facilities, has a mean of approximately 59.92 gigawatts. The exceptionally high standard deviation, nearly 63% of the mean value, indicates a tremendous disparity in the scale of power infrastructure among provincial regions. The vast range from a minimum of 4.97 GW to a maximum of 189.58 GW underscores the significant inequality in energy infrastructure development.

The number of employees in the power industry averages at 96,738 persons per region. The substantial standard deviation (51,765.32) highlights that the size of the workforce is highly variable. This variation is closely linked to the scale of generation assets (installed capacity), the energy mix (e.g., labor-intensive coal power versus more automated renewable sources), and the structure of the local utility sector. The fact that the workforce in the largest region is over 19 times that of the smallest region suggests significant differences in the industry’s role as an employer and the operational models across the country.

Power generation coal consumption with a mean of 65 million tons reflects the heavy reliance on coal for power generation in many regions. The high standard deviation (4165.36) mirrors the pattern of installed capacity, confirming that regions with larger power systems consume vastly larger amounts of coal. The strong correlation between this indicator and CO₂ emissions is expected. The wide range from 6.1 million tons to 176.5 million tons not only indicates economic and population size differences but also likely points to variations in the clean energy mix, with some regions potentially relying almost exclusively on coal while others have diversified their energy sources.

As the primary output of the power industry, electricity generation averages at 221.4 billion kWh. The standard deviation (1434.71) is pronounced, demonstrating that actual power output varies as significantly as the installed capacity. The ratio of generation to capacity can imply the average capacity factor, which is influenced by demand patterns and the type of generation. The minimum and maximum values (2.11 billion kWh to 630.6 billion kWh) represent an extreme contrast in regional electricity demand, driven by factors such as the presence of energy-intensive industries, population size, and overall economic development levels.

CO₂ emissions in the power industry are a critical environmental performance indicator with a mean of approximately 160 million tons of CO₂ per region. The very high standard deviation (10,309.26) shows that environmental impact is highly concentrated in certain areas, directly corresponding to regions with high coal consumption. The maximum value of nearly 437 million tons signifies a major emissions hotspot, which would be a primary target for carbon reduction policy. The significant variation among regions suggests that the carbon intensity of electricity production (tons of CO₂ per kWh) differs greatly across China, highlighting the uneven starting point for different regions in achieving national carbon peaking and neutrality goals.

In summary, the statistics reveal an industry characterized by immense regional disparities in scale, resource consumption, and environmental footprint. The high standard deviations across all indicators confirm that China’s power sector is not monolithic but comprises provinces with vastly different characteristics. This heterogeneity is crucial for policymakers, suggesting that a one-size-fits-all approach to energy transition or efficiency improvement would be ineffective. Instead, region-specific strategies are necessary to address the unique challenges faced by both the largest emitting regions and the smaller regions.

3. Empirical Analysis

3.1. Static Analysis

This study employs MATLAB R2021a software to systematically calculate the green total factor productivity of China’s power industry. Specifically, a super-efficiency SBM model that incorporates undesirable outputs is utilized to estimate the static GTFP of the power industry across 30 provincial regions during the period 2012–2023, with Tibet, Hong Kong, Macao, and Taiwan excluded from the sample. The use of this methodological framework enables a rigorous assessment of efficiency levels while accounting for both desirable economic outputs and the undesirable environmental consequences associated with electricity generation. The results are summarized in Figure 2 and reveal that the majority of provincial GTFP values are below 1, suggesting that the static GTFP of China’s electric power industry remains at a relatively modest level and that substantial room for improvement persists in promoting resource efficiency and reducing environmental impact.

To enhance interpretability and provide a clearer understanding of regional disparities, the average GTFP values for each province are spatially visualized using ArcGIS 10.8.1 software. The resulting distribution is depicted in Figure 3, which highlights distinct spatial patterns of GTFP performance, thereby offering valuable insights into the heterogeneity of green development progress within the power industry across different regions of China.

Overall, the static green total factor productivity of China’s provincial electric power industry remains at a relatively low level, with pronounced disparities observed among provinces and substantial potential for further improvement. These interprovincial differences partially reflect variations in economic development levels, the availability and quality of natural resources, and the technological capacity for energy utilization. Regions with higher economic development tend to have greater investment in advanced power generation infrastructure, renewable energy integration, and energy efficiency technologies, which can enhance the GTFP. Conversely, provinces with lower economic and technological capacity may face constraints in resource allocation, energy efficiency improvements, and adoption of cleaner generation technologies.

During 2012–2023, Jiangsu, Yunnan, Beijing, Zhejiang, and Sichuan ranked among the top five provinces in terms of power industry GTFP. Jiangsu, Beijing, and Zhejiang benefit from relatively high levels of economic development, which provide a solid foundation for substantial investments in power grid infrastructure, renewable energy technologies, and energy-efficient equipment. In addition, these provinces enjoy significant advantages in policy support and technological innovation, enabling them to enhance energy utilization efficiency and reduce carbon emissions through the deployment of advanced generation technologies and environmental protection equipment. Yunnan, with its abundant endowment of clean energy resources, exhibits a high share of renewable power generation and effectively leverages digital grid technology and a shared energy storage system to address the operational challenges associated with a high proportion of renewable energy integration, thereby improving the efficiency of clean energy utilization. Sichuan, characterized by its leading hydropower capacity and generation output, strategically capitalizes on the national “West-to-East Power Transmission” initiative, as well as the coordinated development of the Chengdu–Chongqing region. By transforming its clean energy advantage into a competitive green industry, Sichuan has successfully attracted high-value manufacturing sectors, including hydrogen production and battery manufacturing, which establishes a virtuous cycle of clean energy development, utilization, and industrial upgrading.

From a regional perspective, both the eastern and western provinces demonstrate higher average green total factor productivity in the power industry compared with the central provinces. In the eastern provinces, strong economic capacity facilitates substantial investment in research and development for renewable energy technologies, as well as in carbon mitigation and energy efficiency initiatives. This robust financial and technological support enables the adoption of advanced generation system and clean energy infrastructure, which in turn enhances the overall GTFP. In contrast, the western provinces benefit from abundant natural endowment, including significant hydropower potential and rich wind and solar resources. The high proportion of renewable energy generation in these regions, coupled with innovative grid management and storage solutions, allows for more efficient utilization of clean energy and a reduction in reliance on fossil fuel-based thermal power. By mitigating pollutant emissions from conventional power generation and optimizing the energy output from renewables, these factors collectively contribute to the elevated levels of the GTFP observed in the power industries of both eastern and western provinces. This regional heterogeneity underscores the importance of tailoring energy policy and technological intervention to local economic and resource conditions, ensuring that efforts to improve the GTFP are both efficient and regionally appropriate. The static GTFP of the power industry in 30 provincial-level regions of China from 2012 to 2023 is shown in Figure 2.

3.2. Dynamic Analysis

This study calculates the GML index of the GTFP for China’s electric power industry across 30 provincial regions from 2012 to 2023, and the results are listed in

Table 3. GML index for the power industry in China’s 30 provincial regions.

Region	2012~2013	2013~2014	2014~2015	2015~2016	2016~2017	2017~2018	2018~2019	2019~2020	2020~2021	2021~2022	2022~2023
Beijing	1.111	0.992	1.353	1.010	0.886	1.032	1.043	1.051	0.898	0.987	0.995
Tianjin	1.001	0.941	1.111	0.996	1.065	1.049	1.069	1.126	0.756	1.062	1.006
Hebei	1.025	0.948	0.949	1.020	1.031	1.021	0.985	0.976	1.085	1.181	0.968
Shanxi	1.001	0.951	0.919	0.991	1.038	1.032	0.957	1.013	1.046	1.004	1.088
Inner Mongolia	0.984	0.986	0.946	0.977	1.088	1.104	1.051	0.942	1.080	0.991	1.116
Liaoning	1.033	1.019	1.022	1.044	1.014	0.959	1.099	0.974	1.049	1.011	1.039
Jilin	0.968	1.033	0.991	1.025	1.011	1.127	1.074	1.016	0.992	1.033	1.040
Heilongjiang	0.932	1.022	0.980	1.008	1.009	1.079	1.010	0.996	0.935	1.039	1.025
Shanghai	0.975	0.878	0.991	0.990	1.039	0.994	1.059	1.023	1.069	0.975	1.011
Jiangsu	0.843	0.958	1.005	1.182	0.932	0.979	1.023	0.982	1.100	0.967	1.036
Zhejiang	0.992	0.921	1.066	1.022	0.992	1.019	1.088	0.952	1.118	0.937	1.023
Anhui	0.943	1.013	0.961	0.999	1.048	1.137	0.946	0.945	1.045	1.015	1.098
Fujian	1.007	1.006	0.954	1.029	1.120	1.038	1.083	0.887	1.096	0.889	1.152
Jiangxi	1.056	0.998	1.020	1.057	1.027	1.075	0.993	0.975	1.051	0.949	1.090
Shandong	1.012	1.197	0.903	0.966	0.917	1.048	0.973	1.130	1.070	0.994	0.998
Henan	1.005	0.936	0.919	0.985	1.020	1.058	0.964	0.971	1.019	1.030	1.086
Hubei	0.968	1.159	0.892	1.035	1.126	1.133	0.965	1.115	1.056	1.033	0.945
Hunan	1.021	1.003	0.931	1.022	1.026	1.011	1.056	0.990	1.079	0.918	0.968
Guangdong	0.848	0.987	0.981	1.074	1.114	0.929	1.102	1.009	1.129	0.968	1.096
Guangxi	0.936	1.101	0.972	0.879	1.087	1.120	1.154	0.899	1.015	1.001	1.079
Hainan	1.127	1.070	0.861	1.220	0.952	0.927	1.132	0.916	1.215	0.885	1.205
Chongqing	1.004	1.043	0.946	1.024	1.016	1.064	1.019	1.068	1.116	1.007	1.038
Sichuan	0.956	1.205	1.011	1.055	1.052	1.050	1.103	1.049	0.971	1.022	0.998
Guizhou	0.845	1.086	1.002	0.972	1.087	1.016	1.022	0.989	1.041	0.974	0.993
Yunnan	1.061	1.139	1.071	1.060	1.185	0.965	1.001	0.996	1.000	0.989	1.030
Shaanxi	0.905	0.981	0.901	1.025	1.120	0.974	0.996	1.017	1.092	0.937	0.978
Gansu	0.902	0.912	0.984	0.995	1.050	1.068	1.057	1.062	1.014	0.957	1.064
Qinghai	0.737	0.978	0.916	0.904	1.041	1.258	1.284	1.016	0.849	0.960	1.191
Ningxia	1.048	0.974	0.955	0.922	1.070	1.055	0.966	0.962	1.105	1.049	1.182
Xinjiang	1.034	1.062	1.044	0.979	1.094	1.049	1.078	1.002	1.124	0.972	1.076

. For further analysis, the provincial GML index values are visualized using ArcGIS 10.8.1 software, which is shown in Figure 4.

From an overall perspective, the provincial GML index values are concentrated within the range of 0.96 to 1.05, reflecting a moderate but steady growth trend in the GTFP of China’s electric power industry over the period of 2012–2023. Notably, the GML index of Xinjiang Uygur Autonomous Region is substantially higher than that of most other provinces. Although Xinjiang’s static GTFP baseline is relatively low, this region benefits from abundant renewable energy resources, including wind and solar energy, as well as strong policy support for clean energy development and the optimization of its energy structure. These favorable conditions have facilitated a significant improvement in the GTFP, demonstrating the critical role of resource endowment and proactive policy intervention in driving green productivity. Similarly, some provinces in central and western China, such as Yunnan, Sichuan, and Hubei, exhibit relatively high GML index values. These regions generally possess advantageous resource endowment or were early participants in pilot policy programs, placing them at crucial stages of the energy structure transition. By avoiding an entrenched high-carbon development pathway and making early investment in green technology, these provinces have achieved measurable progress in the sustainable transformation of their electric power industries. In contrast, eastern coastal provinces, including Zhejiang, Jiangsu, and Shanghai, display comparatively lower GML index values, with trends remaining relatively stable over time. This pattern largely reflects the fact that these regions already maintain high static GTFP baselines. Although these regions have advantages in technological innovation and capital investment, further improvement in the GTFP faces greater challenges.

To investigate the primary factors driving changes in green total factor productivity within China’s electric power industry, the GML index is further decomposed into two components, namely EC and BPC, which are respectively listed in

Table 4. EC for the power industry in China’s 30 provincial regions.

Region	2012~2013	2013~2014	2014~2015	2015~2016	2016~2017	2017~2018	2018~2019	2019~2020	2020~2021	2021~2022	2022~2023
Beijing	0.996	1.021	1.041	0.987	1.015	0.998	1.002	1.005	0.972	1.001	1.000
Tianjin	1.104	0.967	1.106	0.934	1.111	1.041	1.095	1.006	0.771	1.124	0.950
Hebei	1.131	0.949	0.984	0.988	1.025	0.977	0.964	0.990	1.053	1.299	0.858
Shanxi	1.129	0.946	0.937	0.953	1.007	1.007	0.931	1.034	1.003	1.010	1.074
Inner Mongolia	1.086	1.008	0.976	0.940	1.045	1.251	1.020	0.987	0.879	0.983	1.107
Liaoning	1.106	1.013	1.018	1.005	1.002	0.946	1.078	0.952	1.049	0.955	1.058
Jilin	0.997	1.005	0.940	1.029	1.003	1.120	1.028	1.002	1.005	0.995	1.027
Heilongjiang	0.997	0.990	1.007	0.995	1.013	1.068	0.983	0.990	0.924	0.997	1.026
Shanghai	1.006	0.991	0.814	0.944	1.043	0.981	1.030	1.040	0.997	0.993	0.983
Jiangsu	0.898	0.998	1.024	1.056	0.979	0.956	1.003	1.046	0.980	0.984	0.970
Zhejiang	1.004	1.002	1.032	0.966	0.998	0.999	0.998	0.963	1.037	0.961	1.010
Anhui	1.041	1.044	0.962	0.940	1.064	1.174	0.888	0.945	0.982	1.033	1.066
Fujian	0.947	1.033	1.004	1.019	1.002	1.026	0.997	0.989	1.004	0.991	1.018
Jiangxi	1.034	0.982	1.056	1.042	0.999	1.076	0.948	0.971	1.002	0.934	1.095
Shandong	1.109	1.336	0.992	0.743	0.942	1.054	0.944	1.187	1.007	1.026	0.924
Henan	1.074	0.933	0.948	0.949	0.999	1.056	0.920	0.974	1.015	0.994	1.086
Hubei	0.922	1.078	0.881	1.015	1.118	1.001	0.936	1.077	0.995	0.999	0.963
Hunan	1.041	1.007	0.859	1.033	1.020	0.980	1.039	1.002	1.057	0.853	0.989
Guangdong	0.923	0.997	0.990	1.022	1.086	0.923	1.089	1.000	1.074	0.979	1.127
Guangxi	0.905	1.045	1.031	0.810	1.082	1.108	1.069	0.911	1.010	0.921	1.090
Hainan	1.105	1.146	0.792	1.223	0.964	0.948	1.057	0.910	1.157	1.008	1.018
Chongqing	1.105	1.041	0.940	1.068	0.960	1.050	0.977	1.080	1.066	1.012	1.073
Sichuan	1.143	1.016	1.011	1.012	0.977	0.996	0.996	1.025	0.987	0.994	1.041
Guizhou	0.873	1.090	1.110	0.889	1.027	0.992	0.971	1.006	0.995	0.974	0.984
Yunnan	1.078	1.110	1.058	0.983	1.010	0.975	0.965	0.952	0.997	1.011	1.024
Shaanxi	0.982	0.992	0.926	0.989	1.176	0.905	0.966	1.014	1.057	0.941	0.963
Gansu	0.926	0.857	1.019	1.026	0.996	1.009	1.041	1.122	0.985	0.893	1.086
Qinghai	0.966	0.913	0.965	0.655	0.973	1.375	1.266	0.953	0.972	0.980	0.973
Ningxia	1.339	0.989	0.873	0.923	1.099	1.095	0.858	0.915	1.054	1.078	1.124
Xinjiang	1.139	1.056	1.126	0.943	1.046	1.018	1.060	1.100	1.048	1.002	0.956

and

Table 5. BPC for the power industry in China’s 30 provincial regions.

Region	2012~2013	2013~2014	2014~2015	2015~2016	2016~2017	2017~2018	2018~2019	2019~2020	2020~2021	2021~2022	2022~2023
Beijing	1.115	0.972	1.300	1.023	0.873	1.034	1.041	1.045	0.924	0.986	0.995
Tianjin	0.907	0.972	1.005	1.067	0.959	1.007	0.977	1.119	0.980	0.945	1.059
Hebei	0.906	0.999	0.965	1.032	1.005	1.045	1.023	0.986	1.031	0.909	1.129
Shanxi	0.886	1.005	0.981	1.040	1.031	1.024	1.028	0.980	1.043	0.994	1.013
Inner Mongolia	0.905	0.979	0.970	1.039	1.041	0.883	1.031	0.955	1.228	1.008	1.008
Liaoning	0.935	1.006	1.004	1.039	1.012	1.014	1.020	1.023	1.001	1.059	0.982
Jilin	0.972	1.028	1.055	0.997	1.008	1.006	1.044	1.014	0.987	1.038	1.013
Heilongjiang	0.936	1.032	0.974	1.012	0.997	1.010	1.028	1.007	1.012	1.042	1.000
Shanghai	0.969	0.886	1.218	1.049	0.996	1.014	1.028	0.984	1.072	0.982	1.028
Jiangsu	0.939	0.960	0.982	1.119	0.952	1.024	1.020	0.939	1.122	0.983	1.068
Zhejiang	0.988	0.919	1.033	1.058	0.995	1.020	1.090	0.988	1.078	0.975	1.013
Anhui	0.906	0.971	0.998	1.062	0.985	0.968	1.065	1.000	1.064	0.982	1.030
Fujian	1.064	0.974	0.950	1.010	1.117	1.012	1.086	0.897	1.092	0.898	1.132
Jiangxi	1.022	1.016	0.966	1.014	1.028	0.999	1.048	1.004	1.049	1.016	0.995
Shandong	0.912	0.896	0.910	1.299	0.974	0.994	1.030	0.952	1.062	0.969	1.080
Henan	0.936	1.003	0.970	1.038	1.021	1.002	1.047	0.997	1.004	1.037	1.000
Hubei	1.050	1.075	1.012	1.020	1.007	1.132	1.031	1.035	1.061	1.035	0.981
Hunan	0.981	0.996	1.084	0.989	1.006	1.032	1.016	0.988	1.021	1.076	0.979
Guangdong	0.918	0.990	0.991	1.051	1.026	1.006	1.011	1.009	1.052	0.989	0.973
Guangxi	1.035	1.054	0.943	1.085	1.005	1.010	1.080	0.987	1.005	1.086	0.989
Hainan	1.020	0.933	1.087	0.997	0.987	0.978	1.070	1.007	1.051	0.878	1.184
Chongqing	0.908	1.001	1.006	0.959	1.058	1.014	1.043	0.989	1.046	0.995	0.967
Sichuan	0.837	1.186	1.000	1.042	1.077	1.054	1.107	1.024	0.984	1.029	0.959
Guizhou	0.969	0.996	0.903	1.093	1.059	1.024	1.053	0.982	1.046	1.000	1.009
Yunnan	0.985	1.026	1.012	1.078	1.173	0.990	1.037	1.046	1.002	0.978	1.006
Shaanxi	0.921	0.989	0.974	1.036	0.953	1.076	1.031	1.002	1.033	0.996	1.015
Gansu	0.974	1.064	0.966	0.969	1.054	1.058	1.015	0.947	1.030	1.071	0.980
Qinghai	0.763	1.071	0.950	1.380	1.070	0.915	1.014	1.066	0.874	0.979	1.224
Ningxia	0.782	0.985	1.094	0.998	0.973	0.964	1.126	1.052	1.048	0.973	1.052
Xinjiang	0.907	1.005	0.927	1.038	1.046	1.031	1.016	0.911	1.072	0.970	1.126

. This decomposition allows for a more detailed understanding of the relative contributions of improvements in operational efficiency and technological progress to overall GTFP growth. The temporal trends of the GML index alongside its decomposed EC and BPC components for the period 2012–2023 are illustrated in Figure 5. Overall, the GML index fluctuates around the value of 1, which reflects a generally moderate growth pattern. Specifically, in eight of the eleven annual intervals, the GML index exceeds 1, which indicates periods of positive GTFP growth. However, only three intervals show slightly lower values, which suggest minor temporary declines. Collectively, these observations demonstrate a sustained upward trajectory in the green productivity of China’s electric power industry, highlighting gradual but consistent progress in its transition toward higher efficiency and more sustainable generation practices over the examined period.

Specifically, when comparing the decomposed components of the GML index, the BPC curve which represents technological progress aligns much more closely with the overall GML trend than the EC curve, particularly during the period 2018–2023. In contrast, the EC curve representing changes in technical efficiency exhibits trends that are sometimes unrelated or even opposite to those of the GML index. Moreover, in the majority of years, the BPC values exceed EC values, which underscores the dominant role of technological progress as the primary driver of improvements in the GTFP of China’s electric power industry. This pronounced influence can be largely attributed to the deep integration of digital technology with power system operation, as well as the rapid iteration and declining cost of clean energy generation technology, which directly enhance operational efficiency in renewable energy generation and promote a green transformation of the power supply side. By contrast, conventional thermal power sectors already benefit from mature resource allocation and relatively high operational efficiency, which leave limited scope for further gains in technical efficiency. On the other hand, from 2012 to 2023, the improvement of the GTFP in China’s power industry is mainly driven by technological progress (BPC), while the phenomenon of relatively lagging change in technological efficiency (EC) is closely related to specific policy orientation during the same period. The policies of additional subsidy and special subsidy for renewable energy electricity price have directly stimulated the research and application of clean technologies by reducing investment risk, promoting the rapid improvement of the BPC. However, its early tendency to focus on construction over operation has led to insufficient investment in refined management of existing assets by enterprises, which has constrained the improvement of the EC. Meanwhile, the electricity market reform at this stage is still in the exploratory stage. The imperfection of the spot and ancillary service markets makes it difficult for price signals to truly reflect the operating cost of the system, weakening the economic driving force for power generation enterprises to improve operational efficiency through flexibility transformation and other means. Market barriers also limit cross provincial optimization of resource allocation, thereby suppressing the improvement of the EC. This indicates that policy tools during this period have achieved significant results in stimulating breakthrough technological innovations, but there are shortcomings in the building market mechanism and management assessment system that promote sustained efficiency improvement. Additionally, the inherent variability of renewable energy generation introduces significant operational and management challenges, and innovative management models to address these complexities are still in development, which limits their immediate impact on the GTFP. It is imperative to consolidate the leading role of technological progress by tackling critical challenges such as the integration of a high share of renewable energy and the development of long-duration energy storage solution. Simultaneously, measures such as establishing a unified national electricity market, improving green power trading mechanism, promoting multi-energy complementarity between coal and renewable energy, and strengthening power sector data governance and talent development can systematically enhance energy resource allocation efficiency and further promote the GTFP of China’s electric power industry. Overall, our research findings regarding the efficiency of the power industry are consistent with the conclusion of Chen X et al. [26], which supports the reliability of the research findings in this article.

4. Policy Recommendations

Based on the above findings, the following recommendations are proposed to enhance the green total factor productivity of the power industry and promote its sustainable development.

• Promote the development of clean energy in a manner tailored to regional conditions and resource endowments. Policymakers and industry stakeholders should design differentiated clean energy strategies that reflect the unique natural, economic, and technological characteristics of each region. Examples include building a regional implementation system led by the National Energy Administration and the National Development and Reform Commission with multi-departmental collaboration, and measuring targets such as the new installed capacity of wind and solar PV power and the minimum output of coal-fired units, to promote the effective connection between installed capacity target and grid consumption. From a regional perspective, in resource-rich areas, particularly in northwestern China, priority should be given to establishing large-scale wind and photovoltaic power bases. These developments should be accompanied by flexibility retrofits of existing coal-fired power units to enhance their peak-shaving and load-balancing capabilities, ensuring stable integration of intermittent renewable energy into the grid. In contrast, in eastern coastal regions, efforts should focus on accelerating the deployment of offshore wind power and exploring the integration of distributed PV system with building infrastructure, creating synergies between urban energy consumption and renewable generation. Such regionally tailored strategies not only facilitate the green transformation of the electric power industry but also contribute to measurable improvements in the GTFP, supporting the broader goals of sustainable energy development and low-carbon transition across different regions in China.
• Strengthen regional coordination and promote complementary electricity sharing across regions. To optimize the utilization of renewable energy resources over a broader spatial scale, a cross-provincial and cross-regional coordination mechanism for electricity consumption should be established, which should integrate both intergovernmental agreement and market-based transaction. Such a mechanism would enable more efficient allocation of clean energy, reduce curtailment of renewable generation, and support grid stability. Simultaneously, the development of a unified national electricity market should be actively promoted, which would create opportunities for emerging market participants, including microgrids, energy storage operators, and load aggregators, to participate in energy trading and ancillary services. It is also necessary to clarify the responsibilities of State Grid Corporation of China and China Southern Power Grid Corporation for cross-regional transmission channel construction and scheduling coordination. The provincial energy regulatory department should implement the connection between the provincial market and cross-provincial transactions and enhance system flexibility and consumption capacity by introducing emerging entities such as load aggregators and energy storage operators to participate in the auxiliary service market. By leveraging these market-driven mechanisms, regions can more effectively balance supply and demand, enhance the integration and utilization of renewable energy, and reduce reliance on conventional fossil-based generation. Collectively, these strategies can systematically increase the operational efficiency and sustainability of the electric power industry, thereby contributing to measurable improvements in the GTFP and supporting the broader goals of a low-carbon and environmentally sustainable energy transition in China.
• Promote innovation in low-carbon energy technology and facilitate the effective commercialization of research outcomes. To promote the development of clean energy in the power system, it is necessary to clarify the division of labor and goals of various power enterprises. Power grid enterprises should focus on improving the cross-regional transmission capacity and intelligence level of distribution networks, increasing the proportion of new energy electricity in ultra-high voltage channels. Power generation enterprises should focus on promoting the flexibility transformation of coal-fired power plants and the construction of large-scale wind and solar bases. New energy enterprises should focus on system-friendly power plant technology research and development and improving the reliable output of power plants, and power equipment enterprises need to achieve an increase in the localization rate of key equipment. All parties need to work together through mechanisms such as technological innovation collaboration, market mechanism improvement, and digital empowerment to jointly build a clean and efficient new-type electric power system. Overall, the electric power industry should intensify its research and development efforts with regard to both energy utilization and low-carbon technology and foster breakthroughs that can support sustainable and efficient energy production. Leveraging major national and regional energy projects, collaborative innovation platforms spanning the entire industry value chain can be established, which can enable the sharing of technological advancements, best practices, and knowledge across enterprises, research institutions, and policymakers. At the same time, designing a robust innovation incentive mechanism can encourage continuous technological development, which can ensure that research outcomes are effectively translated into practical application. Policy instruments such as green finance initiatives, dedicated innovation bonds, and targeted subsidies can guide social and private capital into electric power technology research and development, thereby accelerating the implementation of clean and efficient technologies. Collectively, these strategies will consolidate technological progress as the primary driver of GTFP growth, reinforce the sustainable transformation of power industry, and steadily enhance the GTFP of China’s electric power industry, which can support broader national goals of low-carbon development and environmental sustainability.
• Promote the adoption of green management practices and the digital transformation of power industry. Establishing a comprehensive green management system informed by advanced domestic and international best practices can significantly enhance the operational efficiency and oversight of renewable energy resources. In parallel, the integration of digital technology offers opportunities to optimize the allocation and utilization of power system resources, which can enable more intelligent and responsive management. Cutting-edge tools, such as artificial intelligence, big data analytics, and predictive modeling, can be leveraged to develop a smart energy platform that facilitates seamless coordination across generation, transmission, distribution, storage, and demand-side operations. The platform is led by the National Energy Administration to establish a standardized standard and management system, while the State Grid Corporation of China and China Southern Power Grid Corporation relies on them for the specific construction, data access integration, and daily operation and maintenance of the platform, with the goal of achieving enterprise coverage in the power industry. These platforms can also support sophisticated demand-side management, balance load variability and improve grid stability while accommodating high shares of renewable energy. Collectively, these measures can strengthen operational efficiency and create favorable conditions for the sustained enhancement of the GTFP, ultimately contributing to the broader objectives of low-carbon development, environmental sustainability, and green transformation of China’s electric power industry.

5. Conclusions and Prospects

5.1. Conclusions

Based on data from 30 provincial regions in China for 2012–2023, this study constructs a measurement framework for the GTFP of the power industry. A super-efficient SBM model with undesirable outputs is applied to estimate and analyze static GTFP, while the GML index model is used to evaluate dynamic GTFP. Furthermore, the GML index is decomposed into EC and BPC to identify the respective contributions of technical efficiency and technological progress to GTFP growth. The main findings are as follows:

• The static GTFPs of the power industry across Chinese provinces remain relatively low, with significant interprovincial disparities. Regionally, the average GTFPs in the eastern and western regions are higher than that in the central region.
• The GTFP of China’s electric power industry exhibits a general upward trend. Compared to central and western provinces such as Hainan and Hubei, the GML index in eastern coastal areas, including Zhejiang, Jiangsu, and Shanghai, is relatively low.
• The decomposition analysis of the GML index indicates that the BPC curve aligns more closely with the GML trend. In most years, the BPC values exceed EC values, which indicates that technological progress is the primary driver of the GTFP improvement in China’s electric power industry.

5.2. Limitations and Prospects

Although this study conducted a comprehensive analysis of the GTFP in the Chinese power industry from 2012 to 2023, there are still several avenues for future research. Firstly, further research can utilize more refined data at the city level to capture inter-provincial heterogeneity and provide more precise insights into the driving factors of the GTFP. This study is based on provincial panel data for analysis. Although it is easy to obtain and has wide coverage, it may mask significant differences within the province. There may be significant differences in resource endowment, industrial structure, economic development level, and technological foundation among different cities within a province, and the overall GTFP performance of a province’s power industry may not truly reflect the specific situation within its internal region. This deviation may affect the accuracy of identifying the driving mechanism of the GTFP in the power industry.

Secondly, the analytical framework of this study mainly focuses on the calculation of the GTFP in the power industry related to carbon emissions, which fails to include air pollutants such as sulfur dioxide and nitrogen oxides that have a direct impact on public health when accounting for unexpected outputs. This means that the evaluation dimensions of green development in the power industry are not comprehensive enough and fail to fully reveal its environmental collaborative governance benefits. Future research could consider constructing a comprehensive environmental performance index that includes more environmental pollutants in order to more comprehensively evaluate the multiple impacts of power industry transformation on environmental pollution.

Thirdly, this study mainly focuses on the efficiency of the power production process and fails to include the full lifecycle energy consumption and emissions of upstream and downstream processes such as fuel extraction, equipment manufacturing, power plant construction, and retirement in the analysis framework. This may lead to deviations in the environmental benefit assessment of certain technological routes. Adopting a full lifecycle assessment method can provide a more systematic and fair comparison of the comprehensive environmental performance of different power generation technologies.

Future research can explore the interaction between a high share of renewable energy integration, long-term energy storage, and system flexibility to better understand the technological and operational challenges associated with green transformation. At the same time, spatial econometric methods can be introduced to empirically test the spatial spillover effects of inter-provincial GTFP. This helps to reveal the spatial interaction mechanism of green technology diffusion, factor flow, and environmental regulation, which can provide a theoretical basis for formulating regional collaborative policies. Solving these problems will further deepen our understanding of the dynamic mechanism driving the GTFP growth and provide more effective policies and management strategies for promoting low-carbon and sustainable development in the power industry.