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China's industrial sector generally relies on electricity as its main source of energy, and industrial production can be affected if there are problems with the electricity supply. In order to deal with the uncertain electricity supply and achieve the "dual carbon" target, the industrial sector needs to take effective measures to enhance carbon total factor productivity (CTFP). We use the biennial Luenberger productivity index (BLPI) to try to provide strategies for low-carbon industrial development in China. The results indicate that the overall CTFP of China's industrial sector showed an increasing trend from 2006 to 2019. Technology change was the main contributor to the change in CTFP, but fluctuations in efficiency change remained a challenge. Differences were observed between the light industry sector (LIS) and the heavy industry sector (HIS) in terms of changes in CTFP, with LIS showing more stable changes and HIS experiencing larger fluctuations. Most sub-sectors showed increased CTFP during the sample period. R&D investment and R&D personnel have a positive impact on CTFP, while energy structure is found to hinder CTFP. According to the research results of this study, we offer the corresponding policy implications. This study is the first to explore the balance between low-carbon goals and industrial production from the perspective of improving CTFP, providing a new viewpoint on the contributions of technological innovation to solving socio-economic issues. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is vital for addressing climate change and reducing carbon emissions in emerging economies. Financial development (FD) is crucial for economic growth, and its environmental impact and the adjustment of the industrial structure (IND) is a crucial lever in China's economic transition period. This study explored the relationship between FD, IND, and China's green total factor productivity (GTFEE) from 2000 to 2020 using the super-efficiency SBM-undesirable model, which estimates China's GTFEE. The ARDL results suggest that FD and IND enhance GTFEE in the long term, with FD promoting GTFEE by facilitating industrial structure adjustments. The Dumitrescu–Hurlin panel causality tests supported this finding. The QRPD panel quantile regression and heterogeneity analysis revealed significant heterogeneity in the effects. With increasing GTFEE, FD exerts a restraining effect, gradually weakening and transitioning into a promoting effect, while the IND consistently plays a promoting role. [ABSTRACT FROM AUTHOR]

This paper investigates whether and how tax and fee support policies at the firm level in China influence the total factor energy productivity of enterprises. Using panel data from Chinese public trading companies for the period 2004–2020, this study employs a panel model for estimation. The findings suggest that tax rebates contribute to the improvement of the total factor energy productivity of enterprises. Specifically, a 1% increase in tax refunds leads to a growth of approximately 0.008% in total factor energy productivity. Robustness tests and endogeneity checks confirm the validity of the results. Heterogeneity analysis reveals that tax rebates have a significant impact on state-owned enterprises, small- and medium-sized enterprises, and non-technology firms in terms of enhancing their total factor energy productivity. Mechanism analysis indicates that tax rebates facilitate firms in alleviating financing constraints and enhancing their innovation capabilities, thereby improving energy efficiency. The research findings of this paper provide empirical support for optimizing policy supply, improving energy usage efficiency, and promoting the development of a globally sustainable economy. [ABSTRACT FROM AUTHOR]

This document discusses the challenges and opportunities within the energy industry's industry chain, supply chain, and value chain. It emphasizes the importance of digital transformation, renewable energy sources, and sustainable practices in achieving a low-carbon future. The document includes research papers that cover topics such as carbon emissions, resource management, game theory, market integration, technological innovation, and environmental governance. Overall, the document provides valuable insights and recommendations for policymakers, industry stakeholders, and researchers interested in advancing the global energy transition. [Extracted from the article]

Agricultural green total factor productivity (AGTFP) is an important indicator to reflect the sustainability level of agriculture. The AGTFP network reflects the spatial correlations of the AGTFP among regions; thus, exploring its network structure and influencing factors can provide targeted policy guidance to the coordinated development of the agriculture sector. This study builds an epsilon-based measurement data envelopment analysis (EBM-DEA) model to calculate 31 provincial AGTFPs in China from 2002 to 2020. Then, social network analysis (SNA) was utilized to explore the characteristics of the AGTFP network, and the quadratic assignment procedure (QAP) was applied to find its external influencing factors. We reached four central conclusions: (1) Overall, there was a gradual upward trend of AGTFP in China during 2002~2020, and the average value rose from 0.75 in 2002 to 0.90 in 2020, but there were some differences among regions. (2) There is a complex and stable network characteristic of AGTFP; the average network density is 0.3753, and the average network efficiency is 0.4714. Meanwhile, some eastern and central areas, such as Henan, Anhui, Hubei, Hebei, Jiangsu, etc., have relatively high centrality and are a bridge in the entire network. (3) The AGTFP network can be divided into eight blocks, including two net beneficial blocks (the central-eastern provinces with high centrality); two net spillover blocks, including the provinces located in the developed urban areas (Beijing, Tianjin, Shanghai, and Jilin), the underdeveloped northwest regions (Ningxia, Qinghai, Xinjiang, and Tibet). The other areas are two-way spillover blocks. (4) Transportation development gap, technological progress gap, and the similarities of the agricultural industry structure are critical factors influencing the AGTFP network. Hence, improving the efficiency of the logistics and transportation industry, promoting technology transfer from developed areas to underdeveloped areas, and developing characteristic agriculture are all conducive to promoting the whole region's AGTFP. Our research provides policy implications for sustainable agricultural development in China and other developing countries. [ABSTRACT FROM AUTHOR]

Green development and the high-quality economic growth model have replaced the extensive growth model in an effort to reduce the large amounts of energy consumption and pollution emissions. Green total factor productivity has become an important indicator to more accurately measure the quality of economic growth. Transportation infrastructure is a fundamental component that may effectively integrate regional resources, increase regional cooperation, and encourage the sensible use of resources, and is a key factor in increasing productivity. At present, transportation infrastructure should focus on the speed of construction and the quality level, expand the radiation range of the transportation system, improve the service level of transportation facilities, and promote the spatial coordination between transportation facilities and resources and the environment in each province. Therefore, it is of great significance to study the spatial effect of the transport infrastructure on green total factor productivity in order to understand the role of transport infrastructure and its impact on the quality of economic growth. In this study, the slacks-based measure (SBM) model and the global Malmquist–Luenberger (GML) index were used to calculate the green total factor productivity of 30 provinces in China, while the spatial effect of the transportation infrastructure on green total factor productivity was investigated based on the spatial Durbin model. At the national level, road density, railway density, and road service level show positive spillover effects. The railway service level inhibits the growth of green total factor productivity, and there is obvious regional heterogeneity in transport infrastructure construction in eastern, central, and western regions. Therefore, in the process of transportation infrastructure construction, we should not only pay attention to the scale of expansion but also pursue the quality of service. At the same time, measures such as the flow of talent and the introduction of foreign capital within the region should be constantly coordinated to promote the improvement of green total factor productivity and achieve a win–win situation between economic growth and environmental protection. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality and improve emission reduction efficiency, capturing carbon dioxide from the air on a large scale and promoting the application and innovation of carbon capture technology (CCUS) are the most important goals. This study undertakes an annual and comprehensive evaluation of the policy and the technological innovation efficiency (TIE) of 10 listed companies in China using the DEA model and the Malmquist index analysis method. The number of relevant laws and policies is significant, but they are not well coordinated. The static evaluation results indicate that the complete factor production rate is low, generally lower than 0.9, and the technical innovation efficiency is weak, mainly because of technological backwardness. The dynamic evaluation results indicate that the changes in total factor productivity (TFP) each year are primarily affected by changes in technological progress. This suggests that most domestic enterprises are still exploring technological innovation (TI) and operational business models. Finally, this study proposes measures to improve the TIE of carbon capture technology enterprises in China, including giving full play to the role of the government, expanding effective investment, and improving innovational ability. [ABSTRACT FROM AUTHOR]

The process of carbon neutrality does have economic costs; however, few studies have measured the cost and the economic neutral opportunities. This paper uses a dynamic computable general equilibrium (CGE) model to simulate China's carbon neutrality path from 2020 to 2060 and analyzes its economic impact. This paper innovatively adjusts the CGE modeling technology and simulates the boundary of the Porter hypothesis on the premise of economic neutrality. The results show that the carbon neutrality target may reduce the annual GDP growth rate by about 0.8% in 2020–2060. To make the carbon pricing method under the carbon neutrality framework meet the strong version of the Porter hypothesis (or economic neutrality), China must increase its annual total factor productivity by 0.56–0.57% in 2020–2060; this is hard to achieve. In addition, the study finds that China's 2030 carbon target has little impact on the economy, but the achievement of the 2060 carbon neutrality target will have a significant effect. Therefore, the paper believes that the key to carbon neutrality lies in the coexistence of technological innovation and carbon pricing to ensure that we can cope with global warming with the lowest cost and resistance. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality, the development of new digital infrastructure (NDI) and the improvement of digital capabilities are essential, in order to speed up the transformation of the energy structure. Based on the balanced panel data of 30 provinces in China from 2008 to 2019, we empirically analyzed the impact of NDI on the structural transformation of energy in China and its mechanisms of action. The results demonstrated that (1) NDI had a positive impact on China's energy transition, and the empirical results were robust. (2) The mediating effect showed that NDI had a positive impact on the transformation of energy structure, through improving green total factor productivity and green finance. (3) The heterogeneity analysis indicated that NDI made a more significant contribution to the transformation of the energy structure in regions with lower pollution levels and in those with energy cooperation policies. This study provides a policy reference for Chinese energy transition from the perspective of the digital economy. [ABSTRACT FROM AUTHOR]

As an important energy base in China, Shanxi Province, in its economic development, has depended heavily on coal resources. However, enormous coal consumption produces a large amount of carbon dioxide and an aggravated ecological problem. In this paper, the super efficiency slack-based measure and data envelopment analysis (SBM-DEA) model and Malmquist index were used to calculate the energy consumption and carbon emission efficiency of 11 prefecture-level cities in Shanxi in the period from 2000 to 2020. The results were as follows: (1) The primary form of energy consumption in Shanxi Province is the burning of coal, and the carbon emission efficiencies of the 11 prefecture-level cities are quite different. The technical efficiency, technological progress, and total factor productivity of Taiyuan and Shuozhou were found to be greater than 1 compared to other cities in Shanxi. (2) On the whole, although the carbon emission rate of Shanxi Province has slowed down, it still faces the problems of a large total emission base, high carbon emission intensity, and low efficiency of the unit carbon emission output. Industrial structure is the key factor restricting improvements in the efficiency of carbon emissions in Shanxi Province. (3) From the spatial and temporal evolution of the carbon emission efficiency in Shanxi Province, we can see that the carbon emission efficiency of the whole province gradually dispersed and improved from the central and northern regions to the southern regions during the period of 2000–2020. In order to achieve a low-carbon economy, Shanxi Province must optimize its industrial structure, accelerate research and development in low-carbon technologies, adjust the energy structure, and promote the construction of an energy supply system with clean low-carbon energy as the main source. [ABSTRACT FROM AUTHOR]

In the intersection between economics and engineering, energy economics has been an active research topic for more than 150 years. Balashova and Serletis in [[4]] uncover hidden linkages between the oil price uncertainty, the total factor productivity (TFP) growth, and the critical indicators of knowledge production and associated spillovers. Three papers apply various forecasting techniques in forecasting energy: Gupta and Pierdzioch in [[6]] are forecasting the volatility of crude oil using the LASSO estimator, Hu et al. in [[7]] forecast the Short-Term Load using the Ensemble Empirical Mode Decomposition coupled with the Salp Swarm Algorithm, and Mouchtaris et al. in [[8]] forecast the Natural Gas Spot Prices using an arsenal of Machine Learning Methodologies. [Extracted from the article]

In modern electrical grids, the number of nonlinear grid elements and actively controlled loads is rising. Maintaining the power quality will therefore become a challenging task. This paper presents a power quality mitigation method via smart demand-side management. The mitigation method is based on a genetic algorithm guided optimization for smart operational planning of the grid elements. The algorithm inherits the possibility to solve multiple, even competing, objectives. The objective function uses and translates the fitness functions of the genetic algorithm into a minimization or maximization problem, thus narrowing down the complexity of the addressed high cardinality optimization problem. The NSGA-II algorithm is used to obtain feasible solutions for the auto optimization of the demand-side management. A simplified industrial grid with five different machines is used as a case study to showcase the minimization of the harmonic distortion to normative limits for all time steps during a day at a specific grid node, while maintaining the productivity of the underlying industrial process. [ABSTRACT FROM AUTHOR]

Agricultural mechanization is an important factor to improve the green total factor productivity of the planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of the output-oriented distance function to measure the green total factor productivity of China's planting industry based on net carbon sinks, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China's planting industry. The main findings of this paper are as follows: (1) Agricultural mechanization can promote the planting green total factor productivity significantly, and this basic conclusion is still robust after using instrumental variables and sub sample regression. (2) The path of agricultural mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover, while the mechanisms of operation scale expansion, factor allocation optimization and technical efficiency change are not significant. (3) With the improvement in the mechanization level, the promotion effect of mechanization on planting GTFP will become clearer. Given these findings, the paper adds considerable value to the empirical literature and provides various policy and practical implications. [ABSTRACT FROM AUTHOR]

Power-level control is a crucial technique for the safe, stable and efficient operation of modular high temperature gas-cooled nuclear reactors (MHTGRs), which have strong inherent safety features and high outlet temperatures. The current power-level controllers of the MHTGRs need measurements of both the nuclear power and the helium temperature, which cannot provide satisfactory control performance and can even induce large oscillations when the neutron sensors are in error. In order to improve the fault tolerance of the control system, it is important to develop a power-level control strategy that only requires the helium temperature. The basis for developing this kind of control law is to give a state-observer of the MHTGR a relationship that only needs the measurement of helium temperature. With this in mind, a novel nonlinear state observer which only needs the measurement of helium temperature is proposed. This observer is globally convergent if there is no disturbance, and has the L2 disturbance attenuation performance if the disturbance is nonzero. The separation principle of this observer is also proven, which denotes that this observer can recover the performance of both globally asymptotic stabilizers and L2 disturbance attenuators. Then, a new dynamic output feedback power-level control strategy is established, which is composed of this observer and the well-built static state-feedback power-level control based upon iterative dissipation assignment (IDA-PLC). Finally, numerical simulation results show the high performance and feasibility of this newly-built dynamic output feedback power-level controller. [ABSTRACT FROM AUTHOR]

This paper uncovers linkages between oil price uncertainty, total factor productivity (TFP) growth, and critical indicators of knowledge production and spillovers. It contributes to the literature by investigating the effects of oil price volatility on TFP growth, controlling for two different channels for TFP growth; benefits from the quality of the national innovation system and from adopting new technologies. We use an unbalanced panel for 28 European Union countries for the period from 1990 to 2018. We find that oil price uncertainty has a negative and statistically significant effect on TFP growth, even after we control for technological advancements and the effects of globalization. We also find that the scale of research and innovation and international trade are positive contributors to TFP growth. [ABSTRACT FROM AUTHOR]

In this study, we assumed that 28 European countries (Decision Making Units (DMUs)) aimed to accomplish higher economic outputs, using fewer resources and producing fewer emissions in the form of environmental degradation. In this context, we studied the drivers of total factor productivity change (TFPCH) in DMUs, associated with either managerial capabilities (efficiency change (EC)) or innovations (technical change (TC)) in resource-saving production methods, before and after the integration of CO2 (carbon dioxide) emissions as an additional variable (undesirable output) in the initial model of one output (gross domestic product (GDP)) and five inputs (labor, capital, energy, domestic material consumption and recycled municipal waste). The primary focus of this study is to identify best practices that policymakers can adopt as they attempt to reduce productivity loss. Our results highlight the weak areas of individual countries and seem to indicate the action that should be taken to improve their productivity by taking into consideration the main driving force behind productivity and technical efficiency change. Our findings reveal that an effective use of technological developments is determined as important strategic information for ensuring managerial performance. [ABSTRACT FROM AUTHOR]

This study aims to analyse the international energy trade in EU-15. Within this context, 6 items of international transport and energy trade have been selected to evaluate the performance of European Union (EU) 15 countries. For this purpose, a comparative analysis has been applied by including and excluding the data of energy trade in international transport for the years between 2013 and 2016. In this framework, interval type-2 fuzzy decision-making trial and evaluation laboratory (DEMATEL) and technique for order preference by similarity to ideal solution (TOPSIS) approaches are considered. The findings show that road and sea have the highest importance for international transport and energy trade. However, pipeline and electricity, which represent the energy trade, take place on the last ranks. Another important conclusion is that there is almost no difference in the ranking of international transport performance including and excluding energy trade. While considering these issues, it is recommended that that European countries should take some actions to increase the energy production. The main reason is that it has a positive influence on both international trade volume and industrial production. Thus, they can focus on renewable energy production which leads to lower cost in this process. [ABSTRACT FROM AUTHOR]