Reducing Energy Intensity Research Articles

Spatial-Temporal Evolution and Determinants of Green Economy Efficiency: An Integrated Analytical Approach

In response to global environmental challenges and the need for sustainable development, this study investigates the spatiotemporal evolution and determinants of green economy efficiency (GEE) across 30 Chinese provinces from 2005 to 2021. By employing a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model, kernel density estimation, and spatial Markov chain analysis, this study provides a comprehensive assessment of GEE, incorporating both spatial and temporal dimensions. The analysis reveals a positive overall trend in GEE, with notable regional disparities, as eastern provinces exhibit significantly higher efficiency levels compared to central and western regions. Additionally, GEE improvements have been observed nationwide, although the central and western regions lag. Key factors influencing GEE include energy intensity, economic development, urbanization, industrialization, transport infrastructure, and government intervention, with distinct regional variations in their impact. The findings suggest that optimizing economic growth, reducing energy intensity, and fostering green urbanization and industrialization are essential strategies for enhancing GEE. This research contributes to the theoretical understanding of green economy efficiency and offers valuable policy recommendations for achieving balanced regional development and sustainable growth in China.

An analysis of the rebound impact of energy consumption and the factors that influence it in China's agricultural productivity

With China's agricultural sector being a major contributor to both the national economy and energy consumption, the rebound effect—the phenomenon where energy efficiency improvements lead to increased energy use—poses significant challenges to sustainable energy use. The motivation for this study stems from the need to understand the extent of this rebound impact and its underlying drivers, particularly in the context of China's agricultural modernization efforts. This study examines the rebound effect of energy consumption in China's agricultural productivity and the factors influencing it over the period from 1990 to 2023. Using a Generalized Method of Moments (GMM) model, this research analyzes the relationship between energy consumption, agricultural productivity, and several influencing factors, including technological innovation, rural-urban migration, and financial development. The results reveal that (1) energy efficiency improvements have led to a significant rebound effect in China's agricultural sector, limiting potential energy savings, (2) technological advancements have mitigated the rebound effect to some extent, but the effect remains substantial, (3) rural-urban migration has contributed to labor shortages, increasing mechanization and energy demand, and (4) financial development has positively influenced both agricultural productivity and energy use, exacerbating the rebound effect. The study suggests that policymakers should focus on developing stricter energy efficiency standards and promoting technological innovations that reduce energy intensity in agriculture, while also addressing labor migration challenges to curb the rebound impact and achieve more sustainable agricultural growth.

Dynamic Simulation of Carbon Emission Peak in City-Scale Building Sector: A Life-Cycle Approach Based on LEAP-SD Model

Systematically predicting carbon emissions in the building sector is crucial for formulating effective policies and plans. However, the timing and potential peak emissions from urban buildings remain unclear. This research integrates socio-economic, urban planning, building technology, and energy consumption factors to develop a LEAP-SD model using Shenzhen as a case study. The model considers the interrelationship between socio-economic development and energy consumption, providing more realistic scenario simulations to predict changes in carbon emissions within the urban building sector. The study investigates potential emission peaks and peak times of buildings under different population and building area development scenarios. The results indicate that achieving carbon peaking by 2030 is challenging under a business as usual (BAU) scenario. However, a 10% greater reduction in energy intensity compared to BAU could result in peaking around 2030. The simulation analysis highlights the significant impact of factors such as population growth rate, per capita residential building area, and energy consumption per unit building area and the need for a comprehensive analysis. It provides more realistic scenario simulations that not only enhance theories and models for predicting carbon emissions but also offer valuable insights for policymakers in establishing effective reduction targets and strategies.

Tackling carbon peak and carbon neutrality challenges: A method with long-range energy alternatives planning system and Logarithmic Mean Divisia Index Integration

China aims to carbon peak emissions before 2030 and achieve carbon neutrality by 2060 in response to climate change challenges. Studying the feasibility of this goal, identifying potential gaps, and formulating corresponding emission reduction pathways are urgent issues that need to be addressed. This study creatively integrates Logarithmic Mean Divisia Index into Long-range Energy Alternatives Planning System to assess energy consumption and carbon emissions in Gansu Province from 2001 to 2060, historical and scenario analysis was conducted from a novel perspective. Results indicate that the economic development effect is the most significant driving factor for the emission growth of 131.54 million tons from 2001 to 2020, and its contribution rate is 176.31 %. The leading negative driving factor is the energy intensity effect, contributing at a rate of −62.00 %. Additionally, the energy structure and industrial structure effects also play minor negative roles. Scenario analysis suggests achieving a carbon peak by 2030 requires a 29.53 % reduction in energy intensity from the 2020 baseline, with electricity consumption must reach 46.37 % and clean energy generating 64 %. Carbon peak emissions are projected to be 220.37 million tons, with reduction of carbon emissions by 123.84 million tons by 2060.

Path of Co-control of Pollution and Carbon Emissions Reduction in Typical Parks of Pharmaceutical and Health Industry in Beijing

The agglomeration of carbon emissions from the pharmaceutical and health industry has been growing gradually in Beijing. Conducting research on the path of co-control of pollution and carbon emission reduction in industrial parks is essential to realize synergy between economic development, pollution reduction, and green low-carbon. Based on the production activity data in 2020 of two typical parks of manufacturing and R&D, we selected six measures for co-control of pollution and carbon emission reduction and set up a synergistic development scenario to explore the collaborative development path of different types of parks. The results showed that： ① The primary carbon emission source in the two parks was energy consumption, such as natural gas and electricity, whereas the major pollution source was from key polluting enterprises. The emissions in the R&D park were significantly lower than those in manufacturing parks, with atmospheric pollutant emissions accounting for 25% of the manufacturing park's emissions. ② Energy structure and intensity, along with pollutant emission reduction measures, contributed 62.6% and 37.4% to pollution and carbon emission reduction for the R&D park and 81.6% and 13.5% for manufacturing park, respectively. ③ Adjusting the energy structure of the park and prioritizing the management of key polluting enterprises could achieve synergistic emission reduction, with the rate of emission reduction primarily reflecting atmospheric pollutants. Reducing energy intensity could also facilitate synergistic emission reduction, with a rapid rate of carbon emission reduction. Optimizing the industrial structure could lead to different degrees of pollution and carbon emissions increasing synergistically or not synergistically, owing to the particularity of the industrial structure of the park. ④ Applying measures to adjust the energy structure and reduce energy intensity and pollutant emissions into the collaborative path should be a priority. The governance scope of key polluting enterprises should be appropriately expanded in manufacturing parks. The synergistic effect of public environmental protection facilities of R&D parks should be focused on, in addition to reducing corporate pollutant emissions. Measures to optimize industrial structure at the park level should be adjusted to local conditions and scientifically guide industrial transformation and the settlement of high-tech enterprises.

Oil Price and Energy Intensity Dynamics in Nigeria: Does Technical Change Matter?

It is critical to understand the mechanism needed to control energy intensity especially in an oilexporting country like Nigeria, because of its consequential effect on carbon dioxide emissions and environmental pollution. Increases in energy prices can lead to promotion of better technology and consequently, a reduction in energy intensity through a reduction in energy demand (consumption). This paper explores the dynamics between energy price and energy intensity to reveal the role of technical change in the equation. The study utilizes an autoregressive distributed lag (ARDL) and Toda-Yamamoto approaches. The study sample covers the period 1980 through 2021. The key contribution of this study to the literature is rooted in an understanding of the dynamics of energy intensity and its interplay with technical change in a country study as a critical piece of information for policymakers. The results indicate a change in oil price significantly affects technical innovation. However, there is no link between technical innovation and energy intensity. The plausible justification for the results is the enormity of oil subsidy policy of the Nigerian government.

Carbon emission prediction in a region of Hainan Province based on improved STIRPAT model.

In 2020, China pledged carbon reduction targets at the United Nations: peaking emissions by 2030 and achieving carbon neutrality by 2060. Research and prediction of regional carbon emissions are crucial for achieving these dual carbon targets across China. This study aims to construct an indicator system for regional carbon emissions and utilize it for forecasting. Analyzing carbon emission data from a specific area in Hainan Province from 2010 to 2020, we established an indicator system. Using the interpretable SHAP model, we assessed indicator importance and trends. Employing an improved STIRPAT model with partial least squares regression to address multicollinearity among influencing factors, we developed a carbon emission prediction model. Based on this, we forecasted carbon emissions from 2021 to 2060 in the specified area under three scenarios: natural, baseline, and ambitious. The results show that the growth of resident population and per capita GDP has the most significant promoting effect on carbon emissions in the region while optimizing industrial structure, energy consumption structure, and reducing energy intensity will inhibit carbon emissions. The prediction results indicate that in the natural scenario, regional carbon emissions will peak in 2035, and achieving carbon neutrality by 2060 is not feasible, while the baseline scenario and ambitious scenario can achieve the dual carbon targets on time or even earlier. The research results of this article provide a reference method for predicting carbon emissions in other regions and a guide for future regional emission reduction.

The Impact of Transportation Infrastructure on Manufacturing Energy Intensity: Micro Evidence from High-Speed Railway in China

This paper examines the potential of infrastructure improvements, particularly the operation of High-speed railway (HSR), to enhance the energy efficiency of firms. By regarding the operation of the China’s HSR as an exogenous shock, this paper relies on a difference-in-differences estimation to isolate the causal effect of the infrastructure improvement on manufacturing firms’ energy intensity. This paper finds that the operation of HSR has improved the manufacturing firms’ energy intensity through the catalyzing the proliferation of advanced management techniques, and this effect gradually decreases with the increase in distance between the firm and the HSR station. Moreover, HSR indirectly decreases energy intensity by fostering technological innovation, decreasing debt financing costs, and extending debt financing magnitude. The study also uncovers heterogeneous effects of HSR connectivity on manufacturing firms’ energy intensity, which are more pronounced in smaller cities and industries characterized by high concentration and low agglomeration. Furthermore, the impact of HSR connectivity on reducing energy intensity is more substantial in state-owned and larger firms compared to non-state-owned and smaller firms. JEL Classification: Q43, O13, R41, L60

Spatiotemporal evolution and driving factors of China's carbon footprint pressure: Based on vegetation carbon sequestration and LMDI decomposition

Evaluating carbon footprint pressure (CFP) scientifically and quantifying the environmental impact of economic activities precisely are crucial for informing policies on carbon emission reduction and achieving carbon neutrality in China. This study revealed China's CFP from 2005 to 2021 using data sets of fossil fuel carbon emissions and vegetation carbon sequestration from remote sensing. We investigated the drivers of CFP using LMDI decomposition. Additionally, we used the Tapio model to examine the decoupling of CFP and economic growth. The findings indicate that China's CFP has increased over time and shows a positive spatial correlation. Rapid economic growth and significant population expansion are the primary drivers of the surge in CFP. Conversely, optimizing energy structure, reducing energy intensity, and enhancing ecological quality are key to alleviating the rising pressure on China's carbon footprint. Moreover, we observe a weak decoupling of China's CFP and economic growth. Notably, it is more challenging to achieve an ideal decoupling state in the western region than in the central and eastern regions.

The impact of digital finance on green economic development: A literature review

Green economic development has become an essential pathway to achieving high-quality economic growth, while digital finance represents a prominent trend in global financial advancement. The rapid expansion of digital finance in China has spurred extensive research in this domain. This study systematically combes the literature on digital finance and green economic development. Initially, it delineates the conceptual definition of digital finance, measurement indices and the conceptual definition of green economic development, identifies the key influencing factors. Subsequently, it synthesizes empirical findings on how digital finance influences the growth of green economy from economic, environmental, and energy perspectives. The economic perspective is divided into macroeconomic impacts, mechanisms of influence, and microeconomic aspects; the environmental perspective focuses on the pollution effects and carbon emission reduction attributable to digital finance, and the energy perspective reviews literature on improvements in energy efficiency and reductions in energy intensity driven by digital finance. Finally, the paper offers a comprehensive summary of current research trends and future directions in the field. It emphasizes the need for enhanced digital finance index systems, robust risk control and regulation, and a balanced approach to fostering a genuine green economy. Additionally, it highlights potential future research avenues, such as the effects of digital finance on government policies, foreign trade, environmental pollution, and energy utilization.

Does corporate social responsibility contribute to reducing energy intensity?

Does corporate social responsibility contribute to reducing energy intensity?

The Impact of R&D Expenditures on Regional Energy Intensity in Turkey

Energy efficiency measures are effective solutions for addressing environmental and economic issues, including global warming, energy supply security, and trade deficits from imported energy sources. Turkey through an ambitious policy scheme in the last decades, have reduced energy intensity level significantly, though, still higher than developed countries. In this regard, this study investigates the relationship between R&D expenditures and energy intensity in Turkey through NUTS-I regional data ranging from 2010 to 2021. According to both FMOLS and DOLS technique findings increasing R&D expenditures may significantly decreases energy intensity. Furthermore, economic growth appears to correlate with reduced energy intensity. Additionally, FMOLS findings suggest that industrialization may increases energy intensity, while DOLS results indicate that population growth can also significantly increase energy intensity. Therefore, a customized research and development (R&D) incentivization support system could offer various benefits to Turkey and other developing nations facing similar challenges.

Life cycle perspective in design and product development

Abstract Eco-design is one of the cornerstones of the modern economy, as reflected in the policies aimed at implementing the principles of design for the environment in the European Union legislation. A life cycle perspective is a feature of eco-design. The study aimed to determine whether and to what extent the selected companies operating in manufacturing sectors consider life cycle perspectives when designing and developing the products they offer. The main research area discussed in the following article focused on the question: What kind of activities related to the idea of life cycle thinking in product policy can be identified in the analysed enterprises? Qualitative research was conducted using the individual in-depth interview method with representatives of selected industries located in Poland. Eight manufacturer groups were invited to participate in the study. Based on the recruitment process, 24 companies were chosen for the interview. Individual in-depth interviews were conducted using Microsoft Teams, following the ICC/ESOMAR Code 2016 standards. Based on the results, most companies that participated in the study considered the life cycle perspective when designing or further developing products. However, their activities varied in scope. The activities of the ten interviewed companies could be regarded as advanced. For six companies, the advanced activities targeted the product’s use phase. Two companies undertook such activities at more than one life cycle stage. The study provides evidence that the surveyed companies are beginning to think beyond operational boundaries and changing their pro-environmental orientation, albeit unimpressively. The paper provides evidence that eco-design requirements are an unquestionable driver for activities from a life cycle perspective. All surveyed manufacturers of energy-powered products are taking measures to reduce energy intensity with less activity, for example, ensuring the durability/reliability of products and finding solutions to facilitate disassembly and recycling.

Analysis of Synergistic Drivers of CO2 and NOX Emissions from Thermal Power Generating Units in Beijing–Tianjin–Hebei Region, 2010–2020

Synergistic control of the emissions of air pollutants and CO2 is critical to the dual challenges of air quality improvement and climate change in China. Based on the emission inventories of thermal power units in Beijing, Tianjin, and Hebei, this study analyzes the CO2 and NOX emission characteristics of these units, and identifies and quantifies the synergistic drivers affecting these emission trends. The inventory data show that, between 2010 and 2020, NOX emissions were reduced by 86.1%, while CO2 emissions were reduced by only 29.8%. Although significant progress has been made in reducing NOX emissions through measures such as end-of-pipe treatment, controlling CO2 emissions remains a difficult task. The index decomposition analysis reveals that economic growth is the main driver of CO2 and NOX emission growth, energy intensity reduction is the main driver of CO2 emission reduction, and end-of-pipe treatment is the main driver of NOX emission reduction. Currently, coal occupies about 87% of the energy consumption of thermal power units in the Beijing–Tianjin–Hebei region, and remains the main type of energy for synergistic emissions, and the potential for emission reduction in the energy structure remains huge. For NOX emissions, it is expected that 90% of the reduction potential can be achieved through energy restructuring and end-of-pipe treatment. In conclusion, this high-precision unit-by-unit emission study confirms the effectiveness of the control policy for thermal power units in the region and provides some scientific reference for future policy formulation.

Development of national specialization in 5G technologies within the European Union

Nowadays the efforts of not only companies, but also governments in different countries are aimed at the development of 5G technologies. On the one hand, the introduction of 5G is an expensive process that requires investments in the development, deployment and maintenance of infrastructure, the involvement of a large number of market participants and service providers, the creation of a stimulating and regulatory legislative framework, the provision of radio frequencies of a more efficient spectrum, the solution of patenting issues. On the other hand, the introduction of 5G technologies will be aimed at developing the economy of many industries, creating social benefits, reducing energy intensity, and will create new opportunities for companies. Currently, the benefits of the digital economy can be most used by the United States, the Republic of Korea and China, which have the highest rates of 5G adoption in the world. The countries of the European Union are also interested in the development of 5G technologies. We analysed key 5G technologies for 74,940 related patents for 23 EU countries for the period 2012–2021. It is shown that the leaders in the number of 5G patents were Sweden, Germany, Finland, France, the Netherlands and Ireland. It is noteworthy that countries can either focus on developing a small number of technologies, or develop a wide range of them. Since 2018, France, Estonia and Germany demonstrate the greatest number of technological specializations primarily creating inventions related to 5G data processing. At the same time, the USA, the Republic of Korea and China still retain their competitive advantages in fields of basic electric elements and electric communication technique for fifth generation networks. To improve positions in the global technology market the EU countries need to specialize in rarer and more complex technologies and develop national policies in this area.

TEMOA-europe: An open-source and open-data energy system optimization model for the analysis of the European energy mix

Energy system modeling tools allow to perform comprehensive analyses for the optimal integration of supply and demand technologies in different scenarios. Open tools, in particular, increase the reliability of such tools and their policy relevance. This work aims to present TEMOA-Europe, an open-data and open-software model instance for OECD Europe. Such model is developed on a time scale up to 2050 and calibrated against acknowledged energy statistics up to 2020. This work is the first to present a net-zero emissions by 2050 trajectory envisaging the absence of Russian energy imports starting from 2030. Despite the stringent constraints, TEMOA-Europe is able to provide results for a decarbonization scenario with growing end-use demands – among which some are reduced for the effect of the elasticity to gas price – considering a larger use of renewable source already starting from the near future and reduced energy intensity. Renewable energy represents more than 60 % of total energy supply by 2050 in the computed pathway. The results are also compared to the projections of the International Energy Agency for the Announced Pledges Scenario (since results for the Net-Zero Emissions Scenario are not publicly available), showing large agreement except for the outcomes concerning wind electricity generation.

Green Innovation and Energy Efficiency: Moderating Effect of Institutional Quality Based on the Threshold Model

AbstractRecent studies demonstrated that green innovation and environment-related technologies reduce energy intensity and improve energy efficiency, contributing to the reduction of carbon emissions. However, the existing studies employ linear estimation methods to examine the relationship between green innovation and energy intensity and do not consider the indirect implications of institutional quality for the effect of green technology on energy intensity. Institutional quality is found to be an essential driver of innovation, and countries may need to achieve at least a minimum level of institutional quality to promote green innovation and improve their energy intensity. To test this hypothesis, this paper examines the relationship between energy intensity and green innovation using a panel dataset from 72 countries between 1996 and 2017 and a panel threshold model when institutional quality is considered a threshold variable. The findings highlight that green innovation reduces the energy intensity if and only if countries surpass a certain threshold of institutional quality. Therefore, countries need to improve their institutional quality to promote green innovation and benefit from green technologies in improving their energy intensity.

How does carbon emissions trading affect China’s energy intensity?

The carbon emissions trading policy should be an essential factor influencing the environment and the economy, and should have a crucial impact on energy intensity. This study analyzed data for 29 regions of China from 2005 to 2020, and applied the synthetic control method to assess the impact of carbon emissions trading on energy intensity. The results show that the carbon trading policy significantly reduced energy intensity in the pilot regions. Specifically, from 2013 to 2020, carbon emissions trading decreased energy intensity in Tianjin, Shanghai, Hubei, and Chongqing by 17.92%, 29.22%, 23.89%, and 16.33%. The results of the mechanism test show that carbon emissions trading reduce energy intensity through technology improvements and industrial structure upgrading.

“Minus 1” and energy costs constants: Empirical evidence, theory and policy implications

This paper demonstrates an apparent long-term constancy of economy-wide energy expenditures relative to income – an inter-decadally-constrained sustainable (“Bashmakov-Newbery”) range of 4.2 ± 0.8 % relative to Gross Output, and 7.2 ± 1.5 % relative to GDP, based on data from industrialised countries. Initial evidence suggests the range to be narrower when external trade effects are accounted for. Statistically equivalent to a very-long-term price-to-energy-intensity elasticity of -1 (“Minus 1”), this indicates long-period economic dynamics including induced innovation and structural change, and we probe theories and policy implications. Either higher energy prices are fully offset by reduced energy intensity, or they later decline to match energy intensity improvements. Complementary theoretical approaches help to explain the observations but challenge the conventional economic logic that high environmental pricing should be the principal instrument to drive transformation. Rather, energy efficiency, innovation, deployment, structural change and pricing co-evolve, suggesting need for a diversity of complementary policy strategies implemented over extended periods of time.

The Role of EU Transport Market Liberalization in Shaping Directions of Rail Energy Consumption Rationalization in Relation to the Export of Goods: The Case of Poland

This article discusses the impact of rail market liberalization on the energy intensity of rail in relation to the export of goods, as well as the identification of multidimensional cause-and-effect relationships between rail energy intensity and the importing country’s economic condition, transport performance, and transport distance. Three research questions were formulated: (1) Does the liberalization of the EU transport market and the implementation of a sustainable transport policy contribute to minimizing the energy consumption of rail transport? (2) Does the pursuit of economic growth allow for reducing the energy intensity of goods exported by rail transport in global trade? (3) Is there a justified paradigm for shifting long-distance freight transport from roads to rail? This study concerned 21 directions of the export of goods transported by rail from Poland to partner countries (worldwide) in 2010–2020. A panel model of rail transport energy consumption with random effects was constructed. As a result of rail market liberalization, the export of goods transported by rail across great distances occurs without harming economic development and leads to a reduction in energy intensity. On this basis, key strategies were formulated to promote rail transport in reducing the energy intensity of the transport sector. The authors filled the research gap by identifying the relationship between the energy intensity of exports of goods by rail transport in value terms, depending on the European transport market’s liberalization process, the importing countries’ economic situation, transport volume, and distance. The presented approach is innovative and can be adapted to the analysis of other modes of transport, including road transport, and other countries (and their structure and export directions).