In the current study, a novel dynamic network DEA approach has been employed for the assessment of the efficiency evaluation of energy economy across 11 provinces in eastern China from 2015–2022. The selection of data for 2015–2022 can reflect the trend of China’s energy economy development and efficiency changes, and exclude problems such as inconsistencies in statistical calibre that may exist in earlier data. In the selection of research objects, the provinces in eastern China typically exhibit higher economic density and scale, implying that they display more influence and demonstration guidance in energy consumption, energy infrastructure construction, and energy policy implementation. For the same reason, the study of these provinces can offer a thorough comprehension of the functioning and efficacy of the energy economy in a high energy consumption environment. When selecting input and output indicators, there may be inconsistencies in the statistical calibre of the data collected by different institutions, leading to a decrease in the comparability of the data with each other. In order to solve this problem, we have standardised the data and referred to relevant literature during the data processing process to ensure the comparability and operability between the data. At the same time, some of the data for 2021–2022 are missing, and in order not to affect the quality of the data, we adopted a reasonable interpolation method in the data processing process to ensure that the impact of the interpolation results on the research results is within the acceptable range. The mentioned have been sourced from the National Bureau of Statistics (NBS), the China Provincial Statistical Yearbook, and the Carbon Emission Accounts and Datasets.
As shown in Table 2; Fig. 2, bounded by the intermediate output, i.e., number of R&D patents that reflects scientific and technological progress, this paper divides the evaluation of China’s energy economic efficiency into two stages for research, in which the first stage reflects the efficiency of scientific and technological R&D progress in China’s energy, and the second stage reflects the efficiency of practical transformation of China’s energy scientific and technological achievements. (1)As shown in Fig. 3, in the first stage, regarding the input indicators, this paper refers to the relevant literature research and selects the total energy consumption (10,000 tonnes of standard coal), as well as R&D personnel full-time equivalent (person, year) in human input and the fixed assets investment (billion yuan) in material input, as the input indicators. Total energy consumption refers to the sum of all kinds of energy consumed by a region or a country in a certain period of time, measured in standard coal, the total amount of which directly reflects the energy demand for economic development, and the total energy consumption is negatively correlated with the energy economic efficiency, i.e., the higher the total energy consumption is, the lower the energy economic efficiency is; R&D personnel full-time equivalent is the sum of the workload of all personnel involved in R&D activities, expressed in full-time equivalent, which can reflect the impact of human capital on the development of the energy economy. A large number of R&D personnel indicates that scientific and technological innovation activities are active, which is conducive to promoting the innovation of energy technology and improving the efficiency of energy utilisation, and thus promoting the sustainable development of the energy economy. The fixed assets investment measures the level of investment in infrastructure construction and equipment renewal in a region or country, which is an important driving force for the development of the energy economy, and it can promote energy supply and optimise the energy structure through the construction of energy infrastructure and the development of clean energy, so as to promote the development of the energy economy. Regarding the output indicators of the first stage, this paper chooses to reflect the number of number of R&D patents (pieces) in scientific and technological progress, which can measure the output level of a region or country in scientific and technological innovation, reflecting the impact of scientific and technological innovation on the development of the energy economy, and the number of patents is large, which indicates that scientific and technological innovation is fruitful, and it is conducive to promoting the innovation of energy technology and improving the efficiency of energy use. (2) The intermediate indicators include the output variables of the first stage (carry-over variables) and the input variables of the second stage, which are number of R&D patents (pieces) and completed investment in industrial pollution control (billions of yuan), of which the amount of completed investment in industrial pollution control measures the strength of investment in environmental protection of a region or a country, and a large scale of investment in industrial pollution control indicates that it attaches a high degree of importance to environmental protection and is conducive to the improvement of the Environmental quality. (3) As shown in Fig. 4, in the second stage, regarding the input indicators, this paper takes the number of R&D patents (pieces), which is the output indicator of the first stage of scientific and technological progress, as the input indicator of the second stage, and increases completed investment in industrial pollution control (billions of yuan), which is the financial investment, as the input indicator of the second stage. With regard to the output indicators for the second stage, this paper chooses to use provincial (Municipal) GDP (in billions of yuan), which reflects economic development, the local general public finance budget revenue (in tonnes), which reflects fiscal revenue, and carbon dioxide emissions (in tonnes), which evaluates environmental pollution; provincial (Municipal) GDP refers to the total value of final products and services produced by all resident units in a region during a certain period of time and is measured in billions of yuan. The energy economy needs to be based on the sustainable development of the Gross National Product (GNP), and therefore the energy economy is inextricably linked to the level of economic development. Local general public finance budget revenue measure the level of fiscal revenues of a region or country, reflecting the government’s ability to provide public services and carry out infrastructure construction; a high level of fiscal revenues indicates that the government has sufficient funds to invest in energy infrastructure construction, clean energy development and other areas to provide strong support for the development of the energy economy. Carbon dioxide emissions measure a country or region’s impact on the environment, reflecting the relationship between energy economic development and environmental protection. The reason why this paper chooses carbon emissions as the indicator of non-desired output is that in the existing research on energy and environment, there are many choices of non-desired output, such as sulphur dioxide, nitrogen oxides and other pollutants, but considering the huge impact of carbon constraints on the environment and the economy of countries all over the world, this paper chooses to use carbon emissions as the proxy variable of non-desired output. The metrics for the total phase are shown in Fig. 5.
Static analysis of the CCR-DEA model
This paper analyses and compares the energy economic efficiency of 11 provinces in China in a static direction, dissects the problems of energy economic innovation efficiency, and offers corresponding suggestions. The comparison of the comprehensive efficiency of energy economy innovation from 2015 to 2022 measured by MaxDEA Software is shown in Table 3:
Figure 6 demonstrates the obtained findings implying that China’s comprehensive energy economic efficiency exhibits constant fluctuations and variations, with a peak observed in 2022. Since 2017, however, there is a significant downward trend evident in the average value of comprehensive energy economic efficiency. This downward trend continued until 2020. However, there was a slight improvement observed from 2021 onwards. Additionally, Table 2 shows that the energy economic efficiency between provinces had obvious differences. For example, the mean value of the comprehensive efficiency in Beijing could reach 1, while the comprehensive efficiency of Hebei was only about 0.7. Further observation of Table 2 shows that the output efficiency value of 1 was attained by Beijing, Shanghai, Hainan for eight consecutive years to achieve the effective state of the DEA. The comprehensive efficiency of the performance of the other provinces and cities is also excellent. This paper argues that this good value was achieved due to the strong economic foundation of Beijing and Shanghai as they introduced many talents with the help of their economic advantages. Also, they continuously develop high-tech technology to use technological innovation for reducing the amount of energy consumption and bringing improvements in the energy use efficiency. Hainan, as an important free trade zone in China, has introduced and implemented effective policies. Being China’s second largest island, Hainan is surrounded by the sea. Hence, nuclear energy is an important source of energy in the island, maintained and supported by relevant technical support and energy technology innovation. Fujian, Liaoning, Guangdong, and Jiangsu have an average value of 0.9, which is close to the DEA effective state, thereby indicating that the overall efficiency of the energy economy in these provinces is better. Being large provinces in terms of population, industry, and economy, they consume large amounts of energy, thus leading to continuous technological development and energy efficiency improvement. The average value of output efficiency of Hebei, Shandong, Tianjin and Zhejiang is around 0.8, which is not adequate to reach the DEA effective state. Hebei’s industrial structure is dominated by coal, building materials, cement, and other high-pollution, high energy-consuming industries. Also, Hebei does not pay attention to the development of science and technology. Due to its backward state, it affects the improvement of the overall efficiency of the energy industry. Shandong’s energy consumption is signified as “high carbon”, “unbalanced”, and “non-equilibrium”, with coal consumption accounting for a remarkably high proportion of primary energy consumption. Similarly, the industrial sector, especially.
the energy-intensive industries, consume a huge amount of energy, resulting in a considerably negative impact on the improvement of the comprehensive efficiency of the energy economy. As far as Tianjin is concerned, its comprehensive efficiency of the energy economy showed a low level from 2015 to 2022, mainly due to Tianjin’s over-reliance on foreign investment and failure in making technological innovation, thus resulting in a stagnant state of energy innovation. On the other hand, Zhejiang shows an insufficient energy comprehensive efficiency despite being a large economic province. However, a large population accounts for huge energy consumption and low self-sufficiency rate. Also, the labor force in high-tech technical personnel accounted for a small proportion of the energy innovation with no improvement in the efficiency of the energy innovation. Hence, reasonable labor resources must be provided in the province to improve the comprehensive efficiency of energy.
Analysis of new dynamic network DEA models
Time analysis
While comparing the novel dynamic network DEA model with the CCR-DEA model, it can be noticed that the former reflects the dynamic changes in total factor productivity in the input-output process. At the same time, it also reflects the efficiency of each independent stage and the interconnections between them, assessing all aspects of energy economic efficiency more comprehensively. Figure 7 shows the dynamic trend of total factor productivity of China’s energy economy (2015–2022) from the time level. It also shows that the average value of the total stage of energy economic of total factor productivity in eastern China during the eight-year period is 1.029 – an improvement of 2.9%, indicating that the total factor productivity (as a whole) shows an upward trend. At the same time, the trend of total factor productivity exhibited a pattern of initial decline followed by subsequent increase, reaching its lowest value at 0.992 in 2019–2020, indicating a decrease of 0.8%. This paper argues that the principal reason behind the efficiency regression in this period is the outbreak of the epidemic that led to a decline in China’s energy demand. Indeed, the COVID-19 significantly impacted the energy industry. Between 2021 and 2022, the total factor productivity of 1.074 reached its highest value, an increase of 7.4%, which may be attributed to the development of clean energy and the transformation and upgrading of China’s energy structure in 2022.
To further analyze the mechanism and internal connection of the efficiency changes in each period in the total phase, this paper continues to divide the total efficiency into two phases. However, due to the limitation of space, the specific data of the object under studied will not be included. Only the average value of the Malmquist productivity index of the study period and its index decomposition term (as shown in Table 4) will be collated. By examining the average value of total factor productivity during the two subsequent stages of decomposition, it becomes evident that TFP* experienced an improvement of 12.6%, with an average value of 1.126. Nevertheless, there is a slight decrease of 1.7% in efficiency during the second stage, with an average value of 0.983. Hence, the first stage is the main stage that promotes the improvement of the energy economy in the total stage. Next, the paper attempts to analyze the periods. The total factor productivity of the total phase in the two periods of 2015–2016 and 2021–2022 are 1.065 and 1.074 respectively, which are the two periods with the greatest efficiency improvement in the energy economy during the eight-year period. Further observation of the efficiency of the two phases in these two periods can be obtained. TFP1 in 2015–2016 is 1.298, indicating an improvement of 29.8%. This change signifies that the practical transformation of scientific and technological achievements in the first phase greatly contributed to the improvement of the efficiency value of the total phase. Likewise, TFP1 in 2021–2022 has a value of 1.081, also characterizing the first phase that contributed to the progress of the efficiency value of the total phase in the main phase. The present study, in contrast,
continues to examine the period characterized by a decline in energy economic efficiency. The total factor productivity of the total stage in the two periods of 2018–2019 and 2019–2020 is less than 1. Observation of the two decomposition stages shows that TFP2 in 2018–2019 is 0.983, which is a decline of 1.7%. The regression of total factor productivity in 2018–2019 is primarily attributed to the decline in the efficacy of the second stage of energy science and technology transformation. Compared with TFP1 in 2019–2020, the regression of total factor productivity in this period is likewise due to the decrease in TFP2.
This paper continues to investigate the decomposition of the total factor productivity of the energy economy in two stages of the new dynamic network DEA model. Being the main stage, the first stage promotes energy economic of total factor productivity improvement in the total stage. Table 5 shows its average value of total factor productivity is 1.126 (a 12.6% improvement), and the decomposition of total factor productivity can be obtained. SECH1 is 1.076 (a 7.6% improvement). The analysis indicates that scale efficiency plays a pivotal role in enhancing the efficiency of total factor productivity in the first stage. To conduct an in-depth analysis of the key factors that drive the enhancement of energy economic of total factor productivity, this paper assesses the two periods in which the total factor productivity in the first stage has increased the most. This is to provide valuable insights for periods characterized by low efficiency. The total factor productivity has increased by 29.8% in 2015–2016. It can be further divided into the index of efficiency change and the.
index of technological progress. EFFCH1 in 2015–2016 is 1.067. improved by 6.7%, of which PECH1 improved by 7% and SECH1 improved by 21.9%, indicating that enterprises or economies can effectively utilize economies of scale in the stage of technological progress energy to reduce costs and optimize resource allocation, which greatly improves the efficiency of the energy economy. TFP1 in the period of 2021–2022 was improved by 8. 1%. The decomposition can be obtained as scale efficiency by 8.7%. It clearly implies that economies in the energy sector in 2022 could leverage economies of scale to achieve better economies of scale in terms of energy patent output, lean production, and supply chain optimization in the energy sector.
According to Tables 6 statistics, the average value of total factor productivity during the eight years of the second stage is 0.983, signifying a decrease of 0.7%. Total factor productivity decomposition analysis evidently shows that the scale efficiency of technological energy economy during the period of technological achievement transformation (2015–2022) exhibits a consistent decline year after year, with scale efficiency emerging as the primary constraint on enhancing total factor productivity in the second stage. Further study of the period of the largest decline in total factor productivity demonstrates that TFP2 fell by 1.7% in 2018–2019. Also, its PECH2 was 0.966, signifying a 3.6% decline, which may indicate that the ability of energy enterprises or economies to use existing science and technology in production declined in 2018–2019. Consequently, it led to a decline in the efficiency of energy production. At the same time, the pure technical efficiency decline may have negatively impacted the long-term growth and competitiveness of the energy economy. Therefore, it is the responsibility of the policymakers or industry leaders to enhance the efficiency of the energy economy in the technology transformation stage by increasing investment in R&D and improving management practices. TFP2 declined by 3.5% in 2019–2020, whereas its TECH2 was 0.955, showing a 4.5% decline. It indicates that the energy enterprises or industries in the transformation stage of scientific and technological achievements from theory to practical application had minimal growth in the progress of technological application due to the restriction of personnel mobility during the epidemic period and the shortage of highly skilled personnel. This weakness may also be attributed to the downturn in the global economic situation and the severe investment situation in the market that significantly restricted the research and development and transformation of technology.
index in the first stage.
To conclude, the rise of total factor productivity of energy economy in 2015–2022 primarily benefits from the progress of energy economic efficiency in the first stage, during which the improvement of scale efficiency contributes principally to promoting energy economic of total factor productivity. In the second stage, the total factor productivity obstructs the development of energy economic efficiency in the total stage, which mainly manifests itself in the regression of scale efficiency in the second stage and the decline of the technological progress index in each individual year. The second stage of total factor productivity is a hindrance to the advancement of the efficiency of the energy economy in the overall stage, majorly apparent in the regression of scale efficiency in the second stage and the decline of technological progress index in each year.
Regional analyses
Like the time level, the Malmquist Index of China’s energy economy at each stage is now studied and investigated at the regional level. Table 7 demonstrates the Malmquist Index of energy economy in 11 provinces and cities in China by region and stage from 2015 to 2022. Only Tianjin has a Malmquist index value of 0.983 less than 1, which is a regression of 1.7%, and is ranked last; the rest of the regions are ranked from high to low in terms of their Malmquist Indexes; namely Hebei, Jiangsu, Liaoning, Shandong, Guangdong, Zhejiang, Fujian, Beijing, Shanghai, and Hainan, with a TFP* value of 1.063, 1.061, 1.053, 1.045, 1.033, 1.032, 1.024, 1.014, 1.012, and 1.005 respectively (all greater than 1). Through the comparison of the TFP1 and TFP2 indices, it is evident that 11 provinces and cities, namely, Hebei, Jiangsu, Liaoning, Shandong, Guangdong, Zhejiang, Fujian, Beijing, Shanghai, Hainan, and Tianjin, have lower TFP2 efficiency values. Hence, when compared to the efficiency of China’s energy in the stage of scientific and technological research and development progress, Hebei, Jiangsu, Liaoning, Shandong, Guangdong, Fujian, Beijing, Shanghai, Hainan, and Tianjin must take measures to increase the efforts of pollution prevention and scientific and technological research. These cities and provinces are also necessitated to increase the expenditure and strengthen the efforts of China’s energy scientific and technological achievements in practical transformation.
As shown in Fig. 8, TFP2 < TFP* < TFP1 in 11 provinces and cities, namely Hebei, Jiangsu, Liaoning, Shandong, Guangdong, Zhejiang, Fujian, Beijing, Shanghai, Hainan, and Tianjin. Also, the bending amplitude of TFP* is not obvious among the provinces and cities, thus indicating that the change amplitude of the efficiency value is unobvious, whereas the bending amplitude of TFP1 is obvious. Hence, there is an obvious change of the efficiency value, in accordance with the Malmquist Index (TFP1 ranked from high to low as Hebei, Zhejiang, Beijing, Liaoning, Jiangsu, Fujian, Shandong, Tianjin, Shanghai, Guangdong, Hainan. Among the mentioned, Hebei has the largest TFP1 efficiency value of 1.241 (an improvement by 24. 1%) and Hainan has the smallest TFP1 efficiency value of 1.000. To summarize, none of the efficiency of China’s energy in the stage of progress of science and technology research and development has regressed. However, there are large differences between the regions due their development levels; Hebei, Zhejiang, Beijing, and other regions are significantly better than Shanghai, Guangdong, Hainan, and other regions.The reason for this may lie in the fact that resource-rich regions such as Hebei Province, with abundant energy resources and human resources, provide good basic conditions for S&T R&D. Shanghai, on the other hand, is relatively scarce in resources and relies mainly on external energy input, with high energy dependence on foreign countries and greater pressure on energy security, while regions with a high proportion of traditional industries such as Guangdong Province have relatively little investment in S&T R&D and lower efficiency in the transformation of scientific and technological achievements. When Table 8 is observed, it exhibits the Malmquist Index radar chart of China’s energy economy in 11 provinces and cities by region and stage from 2015 to 2022. In the first stage, there is a slight improvement in the mean value of total factor productivity to 1. 126 improved by 12.6%. On the other hand, with an improvement of 5.7%, the efficiency change index shows a mean value of 1.057. Moving forward, the mean value of pure technical efficiency is 1.044, with a 4.4% improvement. The average scale efficiency value stands at 1.014, signifying a 1.4% increase in efficiency. Likewise, the average technical progress index attains a value of 1.076, marking a 7.6% enhancement over the previous level. The mentioned values characterize the considerably improved efficiency of China’s energy R&D progress stage with a betterment in total factor productivity. However, it cannot be said for the Hainan Province, where there were no similar regressions or progressions. Hebei Province exhibited the greatest progress with 24. 15%.The reason is that Hebei Province actively promotes industrial structure adjustment, vigorously develops clean energy industry, and promotes the transformation and upgrading of traditional industries, which provides a broad application market for energy science and technology research and development. Secondly, Zhejiang Province has made a progress of 18.8%, because Zhejiang Province has a high level of economic development and strong scientific and technological innovation ability, and has abundant scientific and technological talents and technological resources, which can provide technical support for energy science and technology research and development (Table 8). Table 9 shows the Malmquist Index and its decomposition terms for the second stage of energy economy from 2015 to 2022 by region in 11 provinces and cities in China. Table 6, on the other hand, demonstrates that the Malmquist Index of the last-ranked Tianjin is less than 1; the remaining regions show values greater than 1. Hebei, Jiangsu, Shandong, Zhejiang, Fujian, Beijing, and Shanghai are mainly affected by the scale efficiency of TFP2.Therefore, these regions should strengthen internal management and resource allocation optimisation, improve scale efficiency, achieve lean production and supply chain optimisation, reduce costs, and improve the efficiency of technological transformation and energy use. Liaoning, Guangdong, Hainan, and Tianjin Municipality are affected by the second stage of technological progress, and these regions should increase their investment in energy science and technology research and development, and support enterprises, universities, and scientific research institutes to carry out technological research and development, and to enhance their energy science and technology innovation capacity. Among them, Guangdong is also affected by pure technological efficiency. The reason may be that the management level of the energy sector in Guangdong has declined relative to that of other provinces, leading to a decrease in the efficiency of the use of existing technologies and a decrease in pure technological efficiency, so the region should strengthen the internal management of the energy sector or enterprises, improve the level of management, optimise the allocation of resources, and improve the efficiency of technological use. In the first stage, China’s energy economy total factor productivity clearly reflects its growth and development. This study also reflects the science and technology research and development progress in China as well as the regional differences concerning the same, showing the efficiency of the technology provinces.Therefore, the government should formulate differentiated energy science and technology R&D policies according to the resource endowment, economic development level and science and technology innovation capability of each region, and at the same time establish regional cooperation mechanisms to promote the flow and sharing of scientific and technological resources, and jointly promote the progress of energy science and technology R&D in each region. Total factor productivity showed weakness in the second stage. The transformation efficiency has been inhibited by the technological progress index of the regression of China’s energy economy. Hence, in the stage of practical transformation of scientific and technological achievements, each energy enterprise or economy is required to focus on the prevention of pollution, expand and improve scientific and technological research, increase expenditure, and promote technological progress. These measures are imperative to strengthen the practical transformation of Chinese energy scientific and technological achievements. --- news from Nature --- News Original --- China’s energy economic efficiency evaluation based on novel dynamic network DEA In the current study, a novel dynamic network DEA approach has been employed for the assessment of the efficiency evaluation of energy economy across 11 provinces in eastern China from 2015–2022.The selection of data for 2015–2022 can reflect the trend of China’s energy economy development and efficiency changes, and exclude problems such as inconsistencies in statistical calibre that may exist in earlier data. In the selection of research objects, the provinces in eastern China typically exhibit higher economic density and scale, implying that they display more influence and demonstration guidance in energy consumption, energy infrastructure construction, and energy policy implementation. For the same reason, the study of these provinces can offer a thorough comprehension of the functioning and efficacy of the energy economy in a high energy consumption environment. When selecting input and output indicators, there may be inconsistencies in the statistical calibre of the data collected by different institutions, leading to a decrease in the comparability of the data with each other. In order to solve this problem, we have standardised the data and referred to relevant literature during the data processing process to ensure the comparability and operability between the data. At the same time, some of the data for 2021–2022 are missing, and in order not to affect the quality of the data, we adopted a reasonable interpolation method in the data processing process to ensure that the impact of the interpolation results on the research results is within the acceptable range. The mentioned have been sourced from the National Bureau of Statistics (NBS), the China Provincial Statistical Yearbook, and the Carbon Emission Accounts and Datasets. As shown in Table 2; Fig. 2, bounded by the intermediate output, i.e., number of R&D patents that reflects scientific and technological progress, this paper divides the evaluation of China’s energy economic efficiency into two stages for research, in which the first stage reflects the efficiency of scientific and technological R&D progress in China’s energy, and the second stage reflects the efficiency of practical transformation of China’s energy scientific and technological achievements. (1)As shown in Fig. 3, in the first stage, regarding the input indicators, this paper refers to the relevant literature research and selects the total energy consumption (10,000 tonnes of standard coal), as well as R&D personnel full-time equivalent (person, year) in human input and the fixed assets investment (billion yuan) in material input, as the input indicators. Total energy consumption refers to the sum of all kinds of energy consumed by a region or a country in a certain period of time, measured in standard coal, the total amount of which directly reflects the energy demand for economic development, and the total energy consumption is negatively correlated with the energy economic efficiency, i.e., the higher the total energy consumption is, the lower the energy economic efficiency is; R&D personnel full-time equivalent is the sum of the workload of all personnel involved in R&D activities, expressed in full-time equivalent, which can reflect the impact of human capital on the development of the energy economy. A large number of R&D personnel indicates that scientific and technological innovation activities are active, which is conducive to promoting the innovation of energy technology and improving the efficiency of energy utilisation, and thus promoting the sustainable development of the energy economy. The fixed assets investment measures the level of investment in infrastructure construction and equipment renewal in a region or country, which is an important driving force for the development of the energy economy, and it can promote energy supply and optimise the energy structure through the construction of energy infrastructure and the development of clean energy, so as to promote the development of the energy economy. Regarding the output indicators of the first stage, this paper chooses to reflect the number of number of R&D patents (pieces) in scientific and technological progress, which can measure the output level of a region or country in scientific and technological innovation, reflecting the impact of scientific and technological innovation on the development of the energy economy, and the number of patents is large, which indicates that scientific and technological innovation is fruitful, and it is conducive to promoting the innovation of energy technology and improving the efficiency of energy use. (2) The intermediate indicators include the output variables of the first stage (carry-over variables) and the input variables of the second stage, which are number of R&D patents (pieces) and completed investment in industrial pollution control (billions of yuan), of which the amount of completed investment in industrial pollution control measures the strength of investment in environmental protection of a region or a country, and a large scale of investment in industrial pollution control indicates that it attaches a high degree of importance to environmental protection and is conducive to the improvement of the Environmental quality. (3) As shown in Fig. 4, in the second stage, regarding the input indicators, this paper takes the number of R&D patents (pieces), which is the output indicator of the first stage of scientific and technological progress, as the input indicator of the second stage, and increases completed investment in industrial pollution control (billions of yuan), which is the financial investment, as the input indicator of the second stage. With regard to the output indicators for the second stage, this paper chooses to use provincial (Municipal) GDP (in billions of yuan), which reflects economic development, the local general public finance budget revenue (in tonnes), which reflects fiscal revenue, and carbon dioxide emissions (in tonnes), which evaluates environmental pollution; provincial (Municipal) GDP refers to the total value of final products and services produced by all resident units in a region during a certain period of time and is measured in billions of yuan. The energy economy needs to be based on the sustainable development of the Gross National Product (GNP), and therefore the energy economy is inextricably linked to the level of economic development. Local general public finance budget revenue measure the level of fiscal revenues of a region or country, reflecting the government’s ability to provide public services and carry out infrastructure construction; a high level of fiscal revenues indicates that the government has sufficient funds to invest in energy infrastructure construction, clean energy development and other areas to provide strong support for the development of the energy economy. Carbon dioxide emissions measure a country or region’s impact on the environment, reflecting the relationship between energy economic development and environmental protection. The reason why this paper chooses carbon emissions as the indicator of non-desired output is that in the existing research on energy and environment, there are many choices of non-desired output, such as sulphur dioxide, nitrogen oxides and other pollutants, but considering the huge impact of carbon constraints on the environment and the economy of countries all over the world, this paper chooses to use carbon emissions as the proxy variable of non-desired output. The metrics for the total phase are shown in Fig. 5. Static analysis of the CCR-DEA model This paper analyses and compares the energy economic efficiency of 11 provinces in China in a static direction, dissects the problems of energy economic innovation efficiency, and offers corresponding suggestions. The comparison of the comprehensive efficiency of energy economy innovation from 2015 to 2022 measured by MaxDEA Software is shown in Table 3: Figure 6 demonstrates the obtained findings implying that China’s comprehensive energy economic efficiency exhibits constant fluctuations and variations, with a peak observed in 2022. Since 2017, however, there is a significant downward trend evident in the average value of comprehensive energy economic efficiency. This downward trend continued until 2020. However, there was a slight improvement observed from 2021 onwards. Additionally, Table 2 shows that the energy economic efficiency between provinces had obvious differences. For example, the mean value of the comprehensive efficiency in Beijing could reach 1, while the comprehensive efficiency of Hebei was only about 0.7. Further observation of Table 2 shows that the output efficiency value of 1 was attained by Beijing, Shanghai, Hainan for eight consecutive years to achieve the effective state of the DEA. The comprehensive efficiency of the performance of the other provinces and cities is also excellent. This paper argues that this good value was achieved due to the strong economic foundation of Beijing and Shanghai as they introduced many talents with the help of their economic advantages. Also, they continuously develop high-tech technology to use technological innovation for reducing the amount of energy consumption and bringing improvements in the energy use efficiency. Hainan, as an important free trade zone in China, has introduced and implemented effective policies. Being China’s second largest island, Hainan is surrounded by the sea. Hence, nuclear energy is an important source of energy in the island, maintained and supported by relevant technical support and energy technology innovation. Fujian, Liaoning, Guangdong, and Jiangsu have an average value of 0.9, which is close to the DEA effective state, thereby indicating that the overall efficiency of the energy economy in these provinces is better. Being large provinces in terms of population, industry, and economy, they consume large amounts of energy, thus leading to continuous technological development and energy efficiency improvement. The average value of output efficiency of Hebei, Shandong, Tianjin and Zhejiang is around 0.8, which is not adequate to reach the DEA effective state. Hebei’s industrial structure is dominated by coal, building materials, cement, and other high-pollution, high energy-consuming industries. Also, Hebei does not pay attention to the development of science and technology. Due to its backward state, it affects the improvement of the overall efficiency of the energy industry. Shandong’s energy consumption is signified as "high carbon", "unbalanced", and "non-equilibrium", with coal consumption accounting for a remarkably high proportion of primary energy consumption. Similarly, the industrial sector, especially. the energy-intensive industries, consume a huge amount of energy, resulting in a considerably negative impact on the improvement of the comprehensive efficiency of the energy economy. As far as Tianjin is concerned, its comprehensive efficiency of the energy economy showed a low level from 2015 to 2022, mainly due to Tianjin’s over-reliance on foreign investment and failure in making technological innovation, thus resulting in a stagnant state of energy innovation. On the other hand, Zhejiang shows an insufficient energy comprehensive efficiency despite being a large economic province. However, a large population accounts for huge energy consumption and low self-sufficiency rate. Also, the labor force in high-tech technical personnel accounted for a small proportion of the energy innovation with no improvement in the efficiency of the energy innovation. Hence, reasonable labor resources must be provided in the province to improve the comprehensive efficiency of energy. Analysis of new dynamic network DEA models Time analysis While comparing the novel dynamic network DEA model with the CCR-DEA model, it can be noticed that the former reflects the dynamic changes in total factor productivity in the input-output process. At the same time, it also reflects the efficiency of each independent stage and the interconnections between them, assessing all aspects of energy economic efficiency more comprehensively. Figure 7 shows the dynamic trend of total factor productivity of China’s energy economy (2015–2022) from the time level. It also shows that the average value of the total stage of energy economic of total factor productivity in eastern China during the eight-year period is 1.029 - an improvement of 2.9%, indicating that the total factor productivity (as a whole) shows an upward trend. At the same time, the trend of total factor productivity exhibited a pattern of initial decline followed by subsequent increase, reaching its lowest value at 0.992 in 2019–2020, indicating a decrease of 0.8%. This paper argues that the principal reason behind the efficiency regression in this period is the outbreak of the epidemic that led to a decline in China’s energy demand. Indeed, the COVID-19 significantly impacted the energy industry. Between 2021 and 2022, the total factor productivity of 1.074 reached its highest value, an increase of 7.4%, which may be attributed to the development of clean energy and the transformation and upgrading of China’s energy structure in 2022. To further analyze the mechanism and internal connection of the efficiency changes in each period in the total phase, this paper continues to divide the total efficiency into two phases. However, due to the limitation of space, the specific data of the object under studied will not be included. Only the average value of the Malmquist productivity index of the study period and its index decomposition term (as shown in Table 4) will be collated. By examining the average value of total factor productivity during the two subsequent stages of decomposition, it becomes evident that TFP* experienced an improvement of 12.6%, with an average value of 1.126. Nevertheless, there is a slight decrease of 1.7% in efficiency during the second stage, with an average value of 0.983. Hence, the first stage is the main stage that promotes the improvement of the energy economy in the total stage. Next, the paper attempts to analyze the periods. The total factor productivity of the total phase in the two periods of 2015–2016 and 2021–2022 are 1.065 and 1.074 respectively, which are the two periods with the greatest efficiency improvement in the energy economy during the eight-year period. Further observation of the efficiency of the two phases in these two periods can be obtained. TFP1 in 2015–2016 is 1.298, indicating an improvement of 29.8%. This change signifies that the practical transformation of scientific and technological achievements in the first phase greatly contributed to the improvement of the efficiency value of the total phase. Likewise, TFP1 in 2021–2022 has a value of 1.081, also characterizing the first phase that contributed to the progress of the efficiency value of the total phase in the main phase. The present study, in contrast, continues to examine the period characterized by a decline in energy economic efficiency. The total factor productivity of the total stage in the two periods of 2018–2019 and 2019–2020 is less than 1. Observation of the two decomposition stages shows that TFP2 in 2018–2019 is 0.983, which is a decline of 1.7%. The regression of total factor productivity in 2018–2019 is primarily attributed to the decline in the efficacy of the second stage of energy science and technology transformation. Compared with TFP1 in 2019–2020, the regression of total factor productivity in this period is likewise due to the decrease in TFP2. This paper continues to investigate the decomposition of the total factor productivity of the energy economy in two stages of the new dynamic network DEA model. Being the main stage, the first stage promotes energy economic of total factor productivity improvement in the total stage. Table 5 shows its average value of total factor productivity is 1.126 (a 12.6% improvement), and the decomposition of total factor productivity can be obtained. SECH1 is 1.076 (a 7.6% improvement). The analysis indicates that scale efficiency plays a pivotal role in enhancing the efficiency of total factor productivity in the first stage. To conduct an in-depth analysis of the key factors that drive the enhancement of energy economic of total factor productivity, this paper assesses the two periods in which the total factor productivity in the first stage has increased the most. This is to provide valuable insights for periods characterized by low efficiency. The total factor productivity has increased by 29.8% in 2015–2016. It can be further divided into the index of efficiency change and the. index of technological progress. EFFCH1 in 2015–2016 is 1.067. improved by 6.7%, of which PECH1 improved by 7% and SECH1 improved by 21.9%, indicating that enterprises or economies can effectively utilize economies of scale in the stage of technological progress energy to reduce costs and optimize resource allocation, which greatly improves the efficiency of the energy economy. TFP1 in the period of 2021–2022 was improved by 8. 1%. The decomposition can be obtained as scale efficiency by 8.7%. It clearly implies that economies in the energy sector in 2022 could leverage economies of scale to achieve better economies of scale in terms of energy patent output, lean production, and supply chain optimization in the energy sector. According to Tables 6 statistics, the average value of total factor productivity during the eight years of the second stage is 0.983, signifying a decrease of 0.7%. Total factor productivity decomposition analysis evidently shows that the scale efficiency of technological energy economy during the period of technological achievement transformation (2015–2022) exhibits a consistent decline year after year, with scale efficiency emerging as the primary constraint on enhancing total factor productivity in the second stage. Further study of the period of the largest decline in total factor productivity demonstrates that TFP2 fell by 1.7% in 2018–2019. Also, its PECH2 was 0.966, signifying a 3.6% decline, which may indicate that the ability of energy enterprises or economies to use existing science and technology in production declined in 2018–2019. Consequently, it led to a decline in the efficiency of energy production. At the same time, the pure technical efficiency decline may have negatively impacted the long-term growth and competitiveness of the energy economy. Therefore, it is the responsibility of the policymakers or industry leaders to enhance the efficiency of the energy economy in the technology transformation stage by increasing investment in R&D and improving management practices. TFP2 declined by 3.5% in 2019–2020, whereas its TECH2 was 0.955, showing a 4.5% decline. It indicates that the energy enterprises or industries in the transformation stage of scientific and technological achievements from theory to practical application had minimal growth in the progress of technological application due to the restriction of personnel mobility during the epidemic period and the shortage of highly skilled personnel. This weakness may also be attributed to the downturn in the global economic situation and the severe investment situation in the market that significantly restricted the research and development and transformation of technology. index in the first stage. To conclude, the rise of total factor productivity of energy economy in 2015–2022 primarily benefits from the progress of energy economic efficiency in the first stage, during which the improvement of scale efficiency contributes principally to promoting energy economic of total factor productivity. In the second stage, the total factor productivity obstructs the development of energy economic efficiency in the total stage, which mainly manifests itself in the regression of scale efficiency in the second stage and the decline of the technological progress index in each individual year. The second stage of total factor productivity is a hindrance to the advancement of the efficiency of the energy economy in the overall stage, majorly apparent in the regression of scale efficiency in the second stage and the decline of technological progress index in each year. Regional analyses Like the time level, the Malmquist Index of China’s energy economy at each stage is now studied and investigated at the regional level. Table 7 demonstrates the Malmquist Index of energy economy in 11 provinces and cities in China by region and stage from 2015 to 2022. Only Tianjin has a Malmquist index value of 0.983 less than 1, which is a regression of 1.7%, and is ranked last; the rest of the regions are ranked from high to low in terms of their Malmquist Indexes; namely Hebei, Jiangsu, Liaoning, Shandong, Guangdong, Zhejiang, Fujian, Beijing, Shanghai, and Hainan, with a TFP* value of 1.063, 1.061, 1.053, 1.045, 1.033, 1.032, 1.024, 1.014, 1.012, and 1.005 respectively (all greater than 1). Through the comparison of the TFP1 and TFP2 indices, it is evident that 11 provinces and cities, namely, Hebei, Jiangsu, Liaoning, Shandong, Guangdong, Zhejiang, Fujian, Beijing, Shanghai, Hainan, and Tianjin, have lower TFP2 efficiency values. Hence, when compared to the efficiency of China’s energy in the stage of scientific and technological research and development progress, Hebei, Jiangsu, Liaoning, Shandong, Guangdong, Fujian, Beijing, Shanghai, Hainan, and Tianjin must take measures to increase the efforts of pollution prevention and scientific and technological research. These cities and provinces are also necessitated to increase the expenditure and strengthen the efforts of China’s energy scientific and technological achievements in practical transformation. As shown in Fig. 8, TFP2 < TFP* < TFP1 in 11 provinces and cities, namely Hebei, Jiangsu, Liaoning, Shandong, Guangdong, Zhejiang, Fujian, Beijing, Shanghai, Hainan, and Tianjin. Also, the bending amplitude of TFP* is not obvious among the provinces and cities, thus indicating that the change amplitude of the efficiency value is unobvious, whereas the bending amplitude of TFP1 is obvious. Hence, there is an obvious change of the efficiency value, in accordance with the Malmquist Index (TFP1 ranked from high to low as Hebei, Zhejiang, Beijing, Liaoning, Jiangsu, Fujian, Shandong, Tianjin, Shanghai, Guangdong, Hainan. Among the mentioned, Hebei has the largest TFP1 efficiency value of 1.241 (an improvement by 24. 1%) and Hainan has the smallest TFP1 efficiency value of 1.000. To summarize, none of the efficiency of China’s energy in the stage of progress of science and technology research and development has regressed. However, there are large differences between the regions due their development levels; Hebei, Zhejiang, Beijing, and other regions are significantly better than Shanghai, Guangdong, Hainan, and other regions.The reason for this may lie in the fact that resource-rich regions such as Hebei Province, with abundant energy resources and human resources, provide good basic conditions for S&T R&D. Shanghai, on the other hand, is relatively scarce in resources and relies mainly on external energy input, with high energy dependence on foreign countries and greater pressure on energy security, while regions with a high proportion of traditional industries such as Guangdong Province have relatively little investment in S&T R&D and lower efficiency in the transformation of scientific and technological achievements. When Table 8 is observed, it exhibits the Malmquist Index radar chart of China’s energy economy in 11 provinces and cities by region and stage from 2015 to 2022. In the first stage, there is a slight improvement in the mean value of total factor productivity to 1. 126 improved by 12.6%. On the other hand, with an improvement of 5.7%, the efficiency change index shows a mean value of 1.057. Moving forward, the mean value of pure technical efficiency is 1.044, with a 4.4% improvement. The average scale efficiency value stands at 1.014, signifying a 1.4% increase in efficiency. Likewise, the average technical progress index attains a value of 1.076, marking a 7.6% enhancement over the previous level. The mentioned values characterize the considerably improved efficiency of China’s energy R&D progress stage with a betterment in total factor productivity. However, it cannot be said for the Hainan Province, where there were no similar regressions or progressions. Hebei Province exhibited the greatest progress with 24. 15%.The reason is that Hebei Province actively promotes industrial structure adjustment, vigorously develops clean energy industry, and promotes the transformation and upgrading of traditional industries, which provides a broad application market for energy science and technology research and development. Secondly, Zhejiang Province has made a progress of 18.8%, because Zhejiang Province has a high level of economic development and strong scientific and technological innovation ability, and has abundant scientific and technological talents and technological resources, which can provide technical support for energy science and technology research and development (Table 8). Table 9 shows the Malmquist Index and its decomposition terms for the second stage of energy economy from 2015 to 2022 by region in 11 provinces and cities in China. Table 6, on the other hand, demonstrates that the Malmquist Index of the last-ranked Tianjin is less than 1; the remaining regions show values greater than 1. Hebei, Jiangsu, Shandong, Zhejiang, Fujian, Beijing, and Shanghai are mainly affected by the scale efficiency of TFP2.Therefore, these regions should strengthen internal management and resource allocation optimisation, improve scale efficiency, achieve lean production and supply chain optimisation, reduce costs, and improve the efficiency of technological transformation and energy use. Liaoning, Guangdong, Hainan, and Tianjin Municipality are affected by the second stage of technological progress, and these regions should increase their investment in energy science and technology research and development, and support enterprises, universities, and scientific research institutes to carry out technological research and development, and to enhance their energy science and technology innovation capacity. Among them, Guangdong is also affected by pure technological efficiency. The reason may be that the management level of the energy sector in Guangdong has declined relative to that of other provinces, leading to a decrease in the efficiency of the use of existing technologies and a decrease in pure technological efficiency, so the region should strengthen the internal management of the energy sector or enterprises, improve the level of management, optimise the allocation of resources, and improve the efficiency of technological use. In the first stage, China’s energy economy total factor productivity clearly reflects its growth and development. This study also reflects the science and technology research and development progress in China as well as the regional differences concerning the same, showing the efficiency of the technology provinces.Therefore, the government should formulate differentiated energy science and technology R&D policies according to the resource endowment, economic development level and science and technology innovation capability of each region, and at the same time establish regional cooperation mechanisms to promote the flow and sharing of scientific and technological resources, and jointly promote the progress of energy science and technology R&D in each region. Total factor productivity showed weakness in the second stage. The transformation efficiency has been inhibited by the technological progress index of the regression of China’s energy economy. Hence, in the stage of practical transformation of scientific and technological achievements, each energy enterprise or economy is required to focus on the prevention of pollution, expand and improve scientific and technological research, increase expenditure, and promote technological progress. These measures are imperative to strengthen the practical transformation of Chinese energy scientific and technological achievements.