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Energies
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Energy Economics and Environment: Exploring the Linkages
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Energy Economics and Environment: Exploring the Linkages

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 9205

Special Issue Editors

Prof. Dr. Fei Fan

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Guest Editor
School of Economics and Management, Wuhan University, Wuhan 430072, China
Interests: regional economy; urban innovation; sustainable development
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaojun You

E-Mail Website
Guest Editor
School of Geographical Science, Fujian Normal University, Fuzhou 350007, China
Interests: collaborative innovation; regional economy; scientific collaboration; economic geography
Dr. Xionghe Qin

E-Mail Website
Guest Editor
School of Urban and Regional Science, East China Normal University, Shanghai 200062, China
Interests: urban and reginal innovation; economic geography
Dr. Song Wang

E-Mail Website
Guest Editor
School of Business Administration, Northeastern University, Shenyang 110167, China
Interests: urban and regional development; science and technology policy and innovation strategy; sustainable development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Economic development has been commonly viewed as the opposite of energy preservation in the 20th century, forcing humans to choose between the two. However, the dilemma seems unnecessary from the case of China, which proves that energy management could be effectively implemented along with a boost in the economy. China witnesses the energy shift from fossil fuel to renewable energy, which not only lowers the cost and pollution, but also creates new technology, a new market, and new job opportunities. Economic development and energy preservation are not incompatible; instead, there is a win–win situation between them. In fact, energy preservation can potentially improve environmental quality and industrial upgrade, and bring multiple other benefits. Low carbon-emitting contributes to green transition of economic structure, facilitating clean production; encouraging green innovation; accelerating the growth of low-carbon industry; and fostering new edges in renewable energy, green manufacturing, carbon capture, utilization, and storage. This, therefore, can enhance global competitiveness in the industry and the economy.

Thus, a clear understanding of the challenges, opportunities, and relationships in the ongoing process of tackling energy shift is urgently needed to achieve high-end and sustainable development in the economy. All these considerations motivate the proposal of this Special Issue, which aims to collect empirical studies and theoretical contributions which explore the linkages between energy management and economy development. We welcome submissions from energy management, regional Economy, human Geography, and urban studies areas. Our scope also includes, but is not limited to:

  • Quality of economic development under energy constraints;
  • Energy efficiency and green innovation;
  • Carbon emissions and renewable energy;
  • Energy policy assessment;
  • Renewable energy market and carbon neutrality;
  • Coordinated development of economy and energy exploitation.

Prof. Dr. Fei Fan
Dr. Xiaojun You
Dr. Xionghe Qin
Dr. Song Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • energy constraints
  • carbon neutrality
  • economic resilience
  • coordinated development of economy and energy
  • linkages of economic and energy

Published Papers (5 papers)

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Research

26 pages, 2395 KiB  
Article
How Do Financial Development and Industrial Structure Affect Green Total Factor Energy Efficiency: Evidence from China
by Weicheng Xu and Meng Wang
Energies 2024, 17(2), 389; https://0-doi-org.brum.beds.ac.uk/10.3390/en17020389 - 12 Jan 2024
Viewed by 459
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Energy Economics and Environment: Exploring the Linkages)
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17 pages, 1835 KiB  
Article
Risk Spillovers between China’s Carbon and Energy Markets
by Qianrui Hwang, Min Yao, Shugang Li, Fang Wang, Zhenmin Luo, Zheng Li and Tongshuang Liu
Energies 2023, 16(19), 6820; https://0-doi-org.brum.beds.ac.uk/10.3390/en16196820 - 26 Sep 2023
Cited by 1 | Viewed by 699
Abstract
In recent years, with the intensification of global warming and the greenhouse effect, the global consensus has focused on efficient, clean, low-carbon, and green development as a means of achieving new economic growth. China, as a major carbon emitter, has been at the [...] Read more.
In recent years, with the intensification of global warming and the greenhouse effect, the global consensus has focused on efficient, clean, low-carbon, and green development as a means of achieving new economic growth. China, as a major carbon emitter, has been at the forefront of efforts to reduce carbon emissions. The establishment of the carbon emissions trading market, commonly known as the “carbon market”, provides an economic solution for reducing carbon emissions in both the carbon and energy markets. As China’s carbon market continues to grow rapidly, fluctuations in the energy or carbon markets caused by information shocks can easily spread between the two markets, leading to increased interconnectedness. Moreover, the spillover effect of the volatility between China’s carbon market and energy market is not constant, and the intensity and direction of this effect vary depending on different market volatility levels and periods. Therefore, it is crucial to conduct a comprehensive study on the characteristics of the volatility spillover effect between China’s carbon market and energy market and to fully understand the mechanism of energy regulation on carbon prices. This research will have significant practical implications for promoting the establishment of a well-functioning internal price transmission mechanism between China’s carbon market and energy market. This study took the risk spillover between the carbon market and energy market as the research object and systematically combed through its pricing mechanism and spillover impact. Through constructing the DY overflow index model based on a VAR model and generalized variance decomposition method, this study explored the linkage between China’s carbon and energy markets, i.e., the linkage of price fluctuations between China’s energy and carbon markets, as well as the time-varying nature of inter-market spillovers, and provides suggestions on the risk control of price fluctuations between the carbon and energy markets. Full article
(This article belongs to the Special Issue Energy Economics and Environment: Exploring the Linkages)
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39 pages, 5738 KiB  
Article
A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices
by Sun-Feel Yang, So-Won Choi and Eul-Bum Lee
Energies 2023, 16(11), 4271; https://0-doi-org.brum.beds.ac.uk/10.3390/en16114271 - 23 May 2023
Viewed by 1943
Abstract
The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study [...] Read more.
The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study aimed to use machine learning (ML) to predict the Japan Korea Marker (JKM), a spot LNG price index, to reduce price fluctuation risks for LNG importers such as the Korean Gas Corporation (KOGAS). Hence, price prediction models were developed based on long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM) algorithms, which were used for time series data prediction. Eighty-seven variables were collected for JKM prediction, of which eight were selected for modeling. Four scenarios (scenarios A, B, C, and D) were devised and tested to analyze the effect of each variable on the performance of the models. Among the eight variables, JKM, national balancing point (NBP), and Brent price indexes demonstrated the largest effects on the performance of the ML models. In contrast, the variable of LNG import volume in China had the least effect. The LSTM model showed a mean absolute error (MAE) of 0.195, making it the best-performing algorithm. However, the LSTM model demonstrated a decreased in performance of at least 57% during the COVID-19 period, which raises concerns regarding the reliability of the test results obtained during that time. The study compared the ML models’ prediction performances with those of the traditional statistical model, autoregressive integrated moving averages (ARIMA), to verify their effectiveness. The comparison results showed that the LSTM model’s performance deviated by an MAE of 15–22%, which can be attributed to the constraints of the small dataset size and conceptual structural differences between the ML and ARIMA models. However, if a sufficiently large dataset can be secured for training, the ML model is expected to perform better than the ARIMA. Additionally, separate tests were conducted to predict the trends of JKM fluctuations and comprehensively validate the practicality of the ML models. Based on the test results, LSTM model, identified as the optimal ML algorithm, achieved a performance of 53% during the regular period and 57% d during the abnormal period (i.e., COVID-19). Subject matter experts agreed that the performance of the ML models could be improved through additional studies, ultimately reducing the risk of price fluctuations when purchasing spot LNG. Full article
(This article belongs to the Special Issue Energy Economics and Environment: Exploring the Linkages)
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18 pages, 3487 KiB  
Article
Spatiotemporal Dynamics and Topological Evolution of the Global Crude Oil Trade Network
by Xiaoyu Niu, Wei Chen and Nyuying Wang
Energies 2023, 16(4), 1728; https://0-doi-org.brum.beds.ac.uk/10.3390/en16041728 - 09 Feb 2023
Cited by 7 | Viewed by 1688
Abstract
The high separation of crude oil supply and demand markets has led to the formation of a global crude oil trading system. This paper constructs global crude oil trade networks, integrates macro, meso, and micro network analysis methods, combines geospatial visualization techniques, and [...] Read more.
The high separation of crude oil supply and demand markets has led to the formation of a global crude oil trading system. This paper constructs global crude oil trade networks, integrates macro, meso, and micro network analysis methods, combines geospatial visualization techniques, and then portrays the spatiotemporal patterns and topological evolution of the global crude oil trade networks. Thus, it attempts to dig deeper into the world crude oil competition and cooperation links and evolution laws and provides a scientific reference for a comprehensive understanding of the global crude oil market dynamics. The results show that: (1) After three fluctuations of increase and decrease since 2000, the global crude oil trade volume is entering the adjustment period, and the scale of the crude oil market is rising slowly. (2) The international crude oil trade has formed trade network patterns with complex structures, clear hierarchy and unbalanced distribution. The “rich club” phenomenon is significant, with large trading countries dominating the trade network. (3) The scale and density of the global crude oil trade network show a trend of increasing and then decreasing, the network agglomeration pattern becoming more obvious, the inter-nodal links continuously strengthening, and the network connectivity improving. (4) The global crude oil trade networks are characterized by core–periphery structures, and the polarization effect is significant. The US, Russia, China, Japan, the Netherlands, and South Korea hold the core positions in the crude oil trade network, and the major importing countries have become the dominant forces in the trade network. In addition, we present policy suggestions for different types of countries for energy transformation and security in the global trade market system, which can be used as a reference for policymakers. Full article
(This article belongs to the Special Issue Energy Economics and Environment: Exploring the Linkages)
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18 pages, 963 KiB  
Article
Moderating Effect of Financial Development on the Relationship between Renewable Energy and Carbon Emissions
by Yi-Bin Chiu and Wenwen Zhang
Energies 2023, 16(3), 1467; https://0-doi-org.brum.beds.ac.uk/10.3390/en16031467 - 02 Feb 2023
Cited by 6 | Viewed by 1337
Abstract
This study investigates the moderating effect of financial development on the renewable energy–CO2 emissions nexus in OECD countries. We find that both composite financial development and banking sector development have an inverted U-shaped impact on CO2 emissions, while stock market development [...] Read more.
This study investigates the moderating effect of financial development on the renewable energy–CO2 emissions nexus in OECD countries. We find that both composite financial development and banking sector development have an inverted U-shaped impact on CO2 emissions, while stock market development has a U-shaped impact on CO2 emissions. Further, an increase in renewable energy will reduce CO2 emissions, and this reducing impact is affected by different levels of financial development. When promoting financial development, policymakers should pay more attention to its role in enhancing renewable energy, which is related to emissions reduction. Full article
(This article belongs to the Special Issue Energy Economics and Environment: Exploring the Linkages)
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