Special Issue "Economic Analysis on Energy and Environmental Issues and Policy"
Special Issue Editor
Prof. Dr. Ken'ichi Matsumoto
Nagasaki University, Nagasaki, Japan
Interests: Climate change and policy; Global environmental issues; CGE model; Econometric analysis; Agent-based mode; Energy security
Special Issue Information
The current economic system highly depends on energy and natural resources. As a result, economic activities cause various environmental issues, including climate change, pollution, environmental degradation, loss of biodiversity and ecosystem services, waste, and resource depletion. These environmental issues eventually affect our economy, such as industrial activities, agriculture/forestry and land use, disasters, and human health. The global society is now aiming to achieve circular economy as well as a low-carbon, sustainable society. To solve environmental issues and achieve this society, various policy measures have been or will be implemented throughout the world at various levels (global, regional, national, and local). Therefore, it is indispensable to understand the economic aspects of environmental and energy issues and policy (i.e., how environmental and energy issues and policy affect the economy).
This Special Issue aims to gather state-of-the-art research findings and knowledge in all aspects of economic analysis on energy and environmental issues and policy (both empirical and scenario analysis). We also welcome studies with various approaches, from quantitative to qualitative.
Prof. Dr. Ken'ichi Matsumoto
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 papers will be 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 1800 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.
- economic system
- environmental system
- energy system
- sustainable development goals (SDGs)
- low-carbon society
- circular economy
- economy–environment interactions
- economic modeling
- statistical analysis
- policy analysis
This special issue is now open for submission, see below for planned papers
The below list represents only planned manuscripts. Some of these
manuscripts have not been received by the Editorial Office yet. Papers
submitted to MDPI journals are subject to peer-review.
Title: On the Acceptability of Demand Side Management by Time of Day
Abstract: In order to build a low-carbon power system using renewable energy, it is desired to implement demand-side management (DSM) of energy use through energy management system using IoT. If such a DSM is implemented, how will people respond to it? We conducted a questionnaire survey on this question, and we report the results in our study. For example, suppose there is a DSM request during a time when a person is vacuuming. We investigated how many people would respond to changing the vacuuming time, and how much reward could make the DSM work effectively.
Title: Does Carbon Pricing Improve a Company’s Carbon Management Using an Internal Carbon Price? Empirical Evidence from Korean Companies under the Korean Emission Trading Scheme
Abstract: Amid the growing adoption of carbon prices globally, this study examined whether it contributed to corporate carbon management, with particular attention to a company’s internal carbon price, through a case study of Korean companies under the Korean Emissions Trading Scheme. A questionnaire survey targeting mainly energy-intensive industries, i.e., petrochemical, iron & steel, paper & pulp, and on-sight interviews with executive directors of Emissions Trading Scheme division in 10 major companies were carried out. The interviews found that the companies subject to the emissions trading scheme set their own carbon prices based on the domestic carbon market price, and used such to make investment decisions and to build low-carbon funds. Meanwhile, using the data collected from 100 respondents through the survey, Korean companies’ internal carbon pricing was estimated by using a proxy of the emission allowance price at which a company decides to trade emissions in the carbon market. The multiple-bounded discrete choice format was utilized as the analysis method. Results indicate that the range of the internal carbon price varied by sector. Overall, the carbon price internalized by Korean companies under the domestic emission trading scheme corresponds to 14-17 USD/t-CO2. Econometric analysis adopting the internal carbon price as a dependent variable further shows its association with the pre-listed carbon management factors adopted by companies. It revealed that some companies with a high internal carbon price level are incentivized to abate their emissions through emission trading in order to meet their emission cap. Based on the limited resources and studies available on corporate opinions related to carbon strategies, this study revealed valuable insight on progression in corporate strategies to carbon pricing using internal carbon pricing, and further suggests obstacles in the current system to carbon management as well as requirements for the government to improve the related policy in Korea.
Title: The Introduction of Wind Power Generation in a Local Community: An Economic Analysis of Subjective Well-Being Data in Chōshi City
Abstract: In this study, we analyze the external effects of wind turbines, which are often considered detrimental to the promotion of wind power generation. Understanding these externalities is essential for reaching a consensus with residents who live near the planned site of a wind turbine. We conducted a mail survey in Chōshi City in Chiba Prefecture to examine the external effects of wind turbines, adopting a subjective well-being index to measure respondents’ well-being. Regression analysis suggests that a view of wind power turbines has a positive effect on the subjective well-being of local residents. Moreover, results indicate that such well-being increases with increasing distance from wind turbines. In other words, except for scenic elements, we found that wind turbines are not always considered desirable by residents. As such, it is important to further clarify the external influence of wind turbines as well as other facilities in the neighborhood.
Title: Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression
Abstract: Energy risk has always been one of major risk factors for most firms involved in key industrial sectors in both developed and developing countries. Risk management of energy commodities is a crucial issue for majority industrial firms, as it can seriously affect its competitiveness, viability and future profitability. Global economic developments, emerging technological advances and unexpected economic, geopolitical and environmental events have caused a significant increase in volatility of energy commodities prices in the last 20 years. For these reasons, the ability to predict volatility of energy commodities is gaining more and more importance.
In the paper we compare the forecasting performance of the GARCH-type models with support vector regression (SVR) for futures contracts of selected energy commodities: crude oil, natural gas, heating oil, gasoil, gasoline. The GARCH models are a standard tool applied in the volatility literature, while SVR is one of machine learning techniques, which have been gaining huge popularity in recent years.