Understanding the spatial heterogeneity and driving force identification of energy-related CO₂ emissions (ECEs) can help build consensus for mitigating CO₂ emissions and designing appropriate policies. However, previous studies on ECEs that focus on both the global-regional scale and the interaction of factors have been seldom conducted. In this paper, ECE data from 143 countries from 1990 to 2014 were selected to analyze regional differences in ECE growth rates by using the coefficient of variation. Then a geographical detector was used to analyze the key determinant factors on ECE growth rates around the world and in eight types of regions. The results show that: (1) the ECE growth rate in the Organization for Economic Cooperation and Development (OECD) region is low and tended to decrease, while in the non-OECD region it is high and tended to increase; (2) the coefficient of variation and detection factor of ECE growth rates at a regional scale are higher than those at a global scale; (3) in terms of the key determinant factors, population growth rate, growth rate of per capita GDP, and energy intensity growth rate are the three key determinant factors of ECE growth rates in the OECD region and most of the non-OECD regions such as non-OECD European and Eurasian (NO-EE), Asia (NO-AS), non-OECD Americas (NO-AM). The key determinant factors in the African (NO-AF) region are population growth rates and natural gas carbon intensity growth rates. The key determinant factors of the Middle East (NO-ME) are population growth rate, coal carbon intensity growth rate and per capita GDP growth rate; (4) the determinant power of the detection factor, the population growth rate at the global scale and regional scale is the strongest, showing a significant spatial consistency. The determinant power of per capita GDP growth rate and energy intensity growth rate in the OECD region, respectively, rank second and third, also showing a spatial consistency. However, the carbon intensity growth rates of the three fossil fuels contribute little to the growth rate of ECEs, and their spatial coherence is weak; (5) from the perspective of the interaction of detection factors, six detection factors showed bilinear or non-linear enhancement at a global and a regional scale, and the determinant power of the interaction of factors was significantly enhanced; and (6) from the perspective of ecological detection, the growth rate of carbon intensity and the growth rate of natural gas carbon intensity at the global scale and NO-ME region are significantly stronger than other factors, with a significant difference in the spatial distribution of its incidence. Therefore, the OECD region should continue to reduce the growth of energy intensity, and develop alternative energy resources in the future, while those that are plagued by carbon emissions in non-OECD regions should pay more attention to the positive influence of lower population growth rates on reducing the growth rate of energy-related CO₂ emissions. Reducing energy intensity growth rates and reducing, fossil energy consumption carbon intensity. Full article

China has become the world’s largest carbon dioxide (CO₂) emitter. Sectoral production activities promote economic development while also adding considerably to national CO₂ emissions. Due to their different sectoral structures, each region shows different levels of economic development and CO₂ emissions. The Chinese government hopes to achieve the dual objectives of economic growth and CO₂ emissions reduction by encouraging those sectors that have high economic influence and low environmental influence. Based on the above background, this study constructed an inter-regional sectoral economic influence coefficient (REIC) and a CO₂ emissions influence coefficient (RCIC) based on the basic multi-regional input-output (MRIO) model to analyse the economy-carbon nexus of 17 sectors in 30 regions in China in 2010. The results showed that most Chinese sectors and regions had low CO₂ emissions influences in 2010. However, some sectors showed negative environmental influences. Specifically, the mining-related sectors showed high CO₂ emissions influence with low economic influence. It is encouraging that some light industry and high-end equipment manufacturing sectors had low CO₂ emissions influence with high economic influence. For regions, geographic location and past preferential policies are the most important factors influencing local economic growth and CO₂ emissions reduction. Most inland regions have low economic influence with high or low CO₂ emissions influence. Meanwhile, most coastal regions showed high economic influence with low CO₂ emissions influence. Finally, we propose some policy implications for sectors and regions. Full article

Reducing carbon dioxide (CO₂) emissions has become a global consensus in response to global warming and climate change, especially to China, the largest CO₂ emitter in the world. Most studies have focused on CO₂ emissions from the production sector, however, the household sector plays an important role in the total energy-related CO₂ emissions. This study formulates an integrated model based on logarithmic mean Divisia index methodology and a system dynamics model to dynamically simulate household energy consumption and CO₂ emissions under different conditions. Results show the following: (1) the integrated model performs well in calculating the contribution of influencing factors on household CO₂ emissions and analyzing the options for CO₂ emission mitigation; (2) the increase in income is the dominant driving force of household CO₂ emissions, and as a result of the improved standard of living in China a sustained increase in household CO₂ emissions can be expected; (3) with decreasing energy intensity, CO₂ emissions will decrease to 404.26 Mt-CO₂ in 2020, which is 9.84% lower than the emissions in 2014; (4) the reduction potential by developing non-fossil energy sources is limited, and raising the rate of urbanization cannot reduce the household CO₂ emission under the comprehensive influence of other factors. Full article

As the leader of the largest economy, President of the United States has substantive influence on addressing climate change problems. However, a presidential election is often dominated by issues other than energy problems. This paper focuses on the 2016 presidential election, and examines the energy plans proposed by the leading Democrat and Republican candidates. Our data from the Iowa caucus survey in January 2016 suggests that voters were more concerned about terrorism and economic issues than environmental issues. We then compare the Democratic and Republican candidate’s view of America’s energy future, and evaluate their proposed renewable energy targets. We find that the view on renewable energy is polarized between Democratic and Republican candidates, while candidates from both parties agree on the need for energy efficiency. Results from our ordinal least squares regression models suggests that Democratic candidates have moderate to ambitious goals for developing solar and other renewables. The Republican candidates favor fossil fuels and they choose not to provide any specific target for developing renewable energy. In addition, this trend of party polarization has grown more significant when compared with the past three presidential elections. Our observation suggests that energy policies need to be discussed more often regarding the diversification and decarbonization of the nation’s energy system. Full article

As a long term bidding behavior, bid shading is exhibited by wind farms participating in real Uniform Price (UP) markets. This signifies that the wind farm owners bid far below their true long run marginal cost. In this paper, a method is proposed to consider the uncertainty of bidding admission in the long term expected revenue of wind farms. We show that this consideration could perfectly explain the observed bid shading behavior of wind farm owners. We use a novel market price model with a stochastic model of a wind farm to derive indices describing the uncertainty of bidding admission. The optimal behavior of the wind farm is then obtained by establishing a multi objective optimization problem and subsequently solved using genetic algorithm. The method is applied to the analysis of long term bidding behavior of a wind farm participating in a Pay-as-Bid (PAB) auction such as Iran Electricity Market (IEM). The results demonstrate that wind farm owners change their bid shading behavior in a PAB Auction. However, the expected revenue of the wind farm will also decrease in a PAB auction. As a result, it is not recommended to make an obligation for the wind farms to participate in a PAB auction as a normal market player. Full article

There are many uncertainties and risks in residential electricity consumption associated with economic development. Knowledge of the relationship between residential electricity consumption and its key determinant—income—is important to the sustainable development of the electric power industry. Using panel data from 30 provinces for the 1995–2012 period, this study investigates how residential electricity consumption changes as incomes increase in China. Previous studies typically used linear or quadratic double-logarithmic models imposing ex ante restrictions on the indistinct relationship between residential electricity consumption and income. Contrary to those models, we employed a reduced piecewise linear model that is self-adaptive and highly flexible and circumvents the problem of “prior restrictions”. Robust tests of different segment specifications and regression methods are performed to ensure the validity of the research. The results provide strong evidence that the income elasticity was approximately one, and it remained stable throughout the estimation period. The income threshold at which residential electricity consumption automatically remains stable or slows has not been reached. To ensure the sustainable development of the electric power industry, introducing higher energy efficiency standards for electrical appliances and improving income levels are vital. Government should also emphasize electricity conservation in the industrial sector rather than in residential sector. Full article

The extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model has been applied to analyzing the relationship between CO₂ emissions from power industry and the influential factors for the period from 1997 to 2020. The two groups found through partial least square (PLS) regularity test show two important areas for CO₂ emissions reduction from the power industry: economic activity and low-carbon electric technology. Moreover, considering seven influential factors (economic activity, population, urbanization level, industrial structure, electricity intensity, generation structure, and energy intensity) that affect the power CO₂ emissions and the practical situation in the power sector, possible development scenarios for the 13th Five-Year Plan period were designed, and the corresponding CO₂ emissions from the power sector for different scenarios were estimated. Through scenario analysis, the potential mitigation of emissions from power industry can be determined. Moreover, the CO₂ emissions reduction rates in the different scenarios indicate the possible low-carbon development directions and policies for the power industry during the period of the 13th Five Year Plan. Full article

Carbon emissions are the major cause of the global warming; therefore, the exploration of carbon emissions reduction potential is of great significance to reduce carbon emissions. This paper explores the potential of carbon intensity reduction in Beijing in 2020. Based on factors including economic growth, resident population growth, energy structure adjustment, industrial structure adjustment and technical progress, the paper sets 48 development scenarios during the years 2015–2020. Then, the back propagation (BP) neural network optimized by improved particle swarm optimization algorithm (IPSO) is used to calculate the carbon emissions and carbon intensity reduction potential under various scenarios for 2016 and 2020. Finally, the contribution of different factors to carbon intensity reduction is compared. The results indicate that Beijing could more than fulfill the 40%–45% reduction target for carbon intensity in 2020 in all of the scenarios. Furthermore, energy structure adjustment, industrial structure adjustment and technical progress can drive the decline in carbon intensity. However, the increase in the resident population hinders the decline in carbon intensity, and there is no clear relationship between economy and carbon intensity. On the basis of these findings, this paper puts forward relevant policy recommendations. Full article

This study contributes to the realization of intended nationally determined contributions (INDCs) by analyzing their implications for the energy production system and the economy, and determines the role of renewable energies (RE) in reducing the challenge of committing to the INDCs. The Asia-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model was used to assess seven scenarios having the same socioeconomic development but different shares of RE in power generation. By comparing different relative reductions caused by the emission constraints vis-a-vis the business-as-usual (BaU) scenario, the mitigation costs can be estimated. Results show that the economic impact could be reduced by around 55% in terms of welfare loss (from 6.0% to 2.7%) and by around 36% in terms of gross domestic product (GDP) loss (from 3.4% to 2.1%) through the incorporation of high levels of renewable energy. Furthermore, the additional double deployment of wind and SPV to 5.4% and 12.0%, respectively, which currently comprise 43.1% of the renewable energies used in electricity generation, could reduce the GDP loss from 2.1% to 1.9% and reduce the welfare loss from 2.7% to 1.5% in order to achieve a 25.0% GHG emissions reduction. These losses are less than those in the pricing-only scenario (2.1% and 2.3%, respectively). Full article

Climate change policy and sustainable development issues and goals are closely intertwined. Recognizing the dual relationship between sustainable development and climate change points to a need for the exploration of actions that jointly address sustainable development and climate change. Technology transfer is considered an issue with growing interest worldwide and has been recognized as the key in supporting countries to achieve sustainable development, while addressing climate change challenges. This study presents an integrated decision support methodological framework for the formulation and evaluation of activities to promote technology transfer, as well as the provision of clear recommendations and strategies for framing specific policy in the context of climate change. The philosophy of the proposed approach, under the name: assess-identify-define (AID), consists of three components, where each one focuses on a particular problem. The methodology is integrated using appropriate tools in the information decision support system for effective technology transfer (DSS-ΕTT). The pilot application of the proposed methodology, in five representative developing countries, provided the possibility to evaluate the characteristics of the adopted methodology in terms of completeness, usability, extensionality, as well as analysis of results reliability. Full article

Located in East Africa, Uganda is one of the most economically deprived countries that is likely to be dramatically affected by climate change. Over 50% of Ugandan families live in single-roomed overcrowded properties and over 60% of the country’s urban population live in slums. Moreover, the gradual shift towards relatively modern and low thermal resistance building materials, in addition to imminent thermal discomfort due to global warming, may considerably affect the health and wellbeing of low-income people, the majority of whom live in low quality homes with very little or no access to basic amenities. This paper evaluates the effects of various construction methods as well as refurbishment strategies on thermal comfort in low-income houses in Uganda. It is aimed at helping low-income populations adapt to climate changes by developing simple, effective and affordable refurbishment strategies that could easily be applied to existing buildings. Dynamic thermal simulations are conducted in EnergyPlus. The adaptive model defined in BS EN 15251 and CIBSE TM52 is used to evaluate the risk and extent of thermal discomfort. Roofing methods/materials are found to be the key factor in reducing/increasing the risk of overheating. According to the results, roof insulation, painting the roof with low solar absorptance materials and inclusion of false ceilings are, respectively, the most effective and practical refurbishment strategies in terms of improving thermal comfort in low-income houses in Uganda. All refurbishment strategies helped to pass Criterion 3 of CIBSE TM52, as an indicator of “future climate scenarios”, making low-income houses/populations more climate resilient. Full article

In the challenging context of intense negotiations and radical developments in the field of climate policy, informing stakeholders about opportunities and pathways and about scientific insights and warnings is important to help create positive dynamics. Policy makers need digestible information to design good policies, and understand their options and the possible impacts of these options. They need access to well-structured knowledge, as well as appropriate techniques to manage information and data. However, available information is often difficult to access, not in the right format and of limited use to stakeholders. The range of knowledge needs identified has to be effectively addressed by providing interested parties with suitable, to-the-point information, covering the identified gaps. This is the main aim of this article that proposes the design and development of a climate policy database, which contains all the resources that can cover the identified knowledge gaps. The resources are derived from a broad range of existing reports, research and climate policy decisions at different levels. The goal is to render climate policy associated stakeholders able to extract key policy conclusions. The added value of this database was verified by users and stakeholders that generally argued that the climate policy database facilitates solid understanding of climate policy implications and fosters collaborative knowledge exchange in the field. Full article

The influence of context dynamics in the course of the climate change mitigation policy instruments’ (PIs) deployment cycle, usually causes a need for policy adaptation mechanisms to ensure that policies can meet the sector needs efficiently and effectively. In this paper, we argue that important contextual factors are the ones that are perceived to have a great impact over policy effectiveness by key related actors. By examining more thoroughly those effects over PIs, as perceived by policy and market actors, useful feedback on observed policy adaptations can be highlighted. In this context, the aim of this paper is to present a conceptual framework which seeks to investigate the impact of key external factors on policy decision-making. This framework is then applied to policies intended to foster sustainability in the Greek building sector. Contextual parameters that are influential over the effectiveness of the national energy conservation measures are identified through a stakeholder survey. Cluster analysis is then employed for the elicitation of three distinct decision-making priorities’ scenarios. General macroeconomic trends, energy costs, characteristics of the building sector and socio-institutional factors are prioritized differently from various types of actors and induce certain types of PI changes. Distinguishing among the different types of PI change can help explain better under which contextual circumstances policy adaptations occur and provide guidance to other policy makers when found in similar decisional contexts. Full article

This paper discusses energy conservation and emissions reduction (ECER) in China’s power sector. To better understand China’s successes and failures on energy conservation in the electricity industry, first it is important to know the status of China’s power sector, and the key energy conservation actions, as well as the achievements in the past years. Second, two ECER scenarios are constructed to probe the 2020 energy conservation potential. Results show that the potential is estimated to be more than 240 million tons of coal equivalent (Mtce). Third, the improvement of coal power operations, structures and technologies, and ambitious deployment of energy conservation measures are proposed to fully explore the potential of China’s power industry. Fourth, great challenges for China’s ECER and some suggested policies are summed up. The lessons learnt from China will provide a valuable reference and useful inputs for other emerging economies. Full article

The main function of an airport is to provide access to air transport both for passengers and cargo. The number of air operations over the past 20 years has increased rapidly, and this has led to a rise in the energy needs of airports to satisfy this demand. As a consequence, the cost of energy supply for airport managers has escalated. At the same time, global energy consumption has soared due to the needs of emerging countries like China and India, with the consequent environmental impact. This complex scenario of environmental and economic factors has made airport managers become aware of the need to reduce energy consumption as well as a more efficient use of it. The aim of this article is to analyze the main behaviors and energy trends at airports in more recent research, starting with the description of the main energy sources and consumers, the application of energy conservation and energy efficiency measures, the establishment of energy indicators and benchmarking between airports, as well as energy modeling and simulation. Full article