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AbstractThis research work analyzes the relationship between environmental degradation, economic growth, trade openness, primary energy consumption, coal consumption, and hydroelectricity consumption in Turkey from 1971 to 2015 using the autoregressive distributed lag (ARDL) time series approach through the hypothesis of the environmental Kuznets curve (EKC). Carbon dioxide (CO2) emissions and ecological footprint are both used as indicators of environmental degradation, which employs six different models. According to the results found in this study, while trade openness increases CO2 emissions, it decreases ecological footprint in the long-run. Coal consumption raises both CO2 emissions and ecological footprint. While hydroelectric energy reduces CO2 emissions, it has no effect on the environment. The results demonstrate that the EKC hypothesis is correct for both CO2 emissions and Turkey’s ecological footprint. The threshold points are investigated as $18,704, $16,361, and $13,571 in models, where CO2 emissions are the dependent variable. In models where the ecological footprint is the dependent variable, the investigated threshold points of $11,824, $11,821, and $15,476 are higher than the gross domestic product (GDP) per capita during the analysis periods. Furthermore, the findings highlight the importance of renewable energy use in reducing environmental degradation and coal use in increasing environmental degradation. These findings can shed light on the importance of transition to renewable energy sources (i.e., hydroelectricity consumption), from fossil fuels (i.e., coal consumption), related to future planning in energy diversification for Turkey.

This study investigates the validity of the EKC (environmental Kuznets curve) hypothesis for the 50 US states and a Federal District (Washington, D.C.). To this aim, the common correlated effects (CCE) and the augmented mean group (AMG) estimation procedures are applied between 1980 and 2015. While the CCE estimation does not support EKC hypothesis, the AMG does. The empirical findings of the AMG estimation indicate that only 14 states verify the EKC hypothesis. Additionally, the expected negative impacts of fossil energy consumption on the environment (CO2 emissions) are strongly detected in all states except Texas. However, the expected positive impacts of renewable energy consumption on the CO2 emissions are detected only in 13 states. Furthermore, the expected negative impacts of the population are not detected in some mostly populated states like New York, Texas, and Ohio. The overall findings of this study may help the US state-level policy makers in two ways: first, to understand whether their economic growths are sustainable (eco-friendly); second, to see how their fossil and renewable energy consumptions affect their environments and to review their energy policies.

Energy justice is violated when particular customers and locations are excluded from a variety of urban energy service distribution. This study explores energy justice in terms of natural gas distribution by providing empirical evidence from 356 neighborhoods of Izmir Metropolitan Area (IMA). The aim is to reveal driving factors of natural gas investment and the spatial reflections of the relationships between investment, and socio-economic and physical characteristics. A global regression model, OLS, and two local spatial regression models, GWR and MGWR, are conducted. Population, income, employment and disadvantaged areas are the significant determinants of natural gas investments. Due to the presence of spatial autocorrelation in OLS residuals, GWR and MGWR are utilized to account for spatial variation in the response variable. Local models are superior to the global model according to AICc values, which are 607.3, 586.7, and 558.5, in OLS, GWR, and MGWR, respectively. MGWR further improves the overall fit with higher R-sq and lower AICc values. The local R-sq values indicate at least 70% variability is explained in 85% of the study area in MGWR, and in 73% of IMA in GWR. Parameters are slightly overestimated in GWR at the mean level. None of the local models are subject to multicollinearity according to local condition numbers, but local variance decomposition proportions indicate the effects of multicollinearity in some observations. Spatial modeling of investments helps to demonstrate local variations of energy injustice and to develop site-specific policies. Population and employment are related to potential customers, which lead to higher investments. Income depends on purchasing power, and there are economic barriers that need to be regulated through subsidies and incentives for the low-income households. Awareness raising policies can also be developed to better inform households about energy alternatives. Disadvantaged areas, either declared as urban transformation areas or currently under urban transformation, lack the natural gas service in general, while the investments can be considered in those areas along with the new development plans.

Economic development is characterised by natural resource extraction and consumption. However, due to the finite nature of fossil fuel energy sources and its price shocks, an investigation into its historical fluctuations is essential for energy policy formulation. Against the backdrop, this paper examines the stationary properties of coal, oil and natural gas consumption per capita of 16 Organisation for Economic Co-operation and Development (OECD) countries for the period 1970-2018. The study employs Fourier ADF and Fourier KSS unit root tests for linear and nonlinear series to assess the permanent or transitory shocks in coal, oil, and natural gas consumption. Empirical findings show that coal consumption is stationary for 6 of 16 countries. In contrast, oil consumption is found stationary for 4 of 16 countries while natural gas consumption is found stationary for 5 of 16 countries. These results demonstrate that any shock in oil, coal, and natural gas consumption will be permanent in most of the OECD countries. Thus, finding fossil fuel alternatives like renewable energy sources which are localized rather than internationally tradable, lessens the reliance on fossil fuel imports and the negative impacts of price shocks.

Global warming has become one of the most critical factors affecting the world, especially in the last decade. Therefore, it is of great importance to analyze the impact of global warming and take measures. The main factor leading to global warming is considered to be people’s consumption and production behaviors. The primary indicator of this is greenhouse gases. Relevant policy changes need to be made to control greenhouse gases. In this context, it is necessary to determine the differences in greenhouse gas emissions at the national level. To identify these differences, this study applies the convergence hypothesis, which has been the subject of numerous researchers since the 1980s. In this study, we analyzed the greenhouse gas intensity convergence for countries in the Organization for Economic Cooperation and Development (OECD) using linear and nonlinear panel unit root tests. The results of this study show that the greenhouse gas emissions in the OECD countries do not converge to the OECD average.

The well-functioning domestic institutions are very important for the global value chains to have its positive impact on environment. This article provides an empirical assessment of the impact of domestic institutional arrangements along the way to participate in global value chains on environmental performance index for sustainability goals for 41 Asian countries over 2001–2018 period. Most recent empirical studies assumes that causality runs to environmental measures through trade and governance but inverse relationship is also feasible and none of the previous studies have discussed about it. Using instrumental variable strategy to closing these gaps, we analyse the mechanism of direct and indirect impact of participation in global value chains on environmental performance index. Our results show that environmental performance index is negatively affected by participation in global value chains and this relationship overturns when participation in global value chains is accompanied by governance facilitation. Further, the findings suggest that governance-augmented participation in global value chains is a tool of environmental sustainability.

Abstract The amount of household solid waste (HSW) has been significantly increasing due to a rapid population growth and economic development. HSW management is immensely sensitive and complicated problem not only in rapidly developing countries like Uzbekistan but also in developed countries with advanced economies.The accumulated waste has been causing a number of serious environmental problems such as release of the most dangerous greenhouse gases (CO2, N2O, CH4) in the atmosphere which misbalance radiation in a planet’s atmosphere causing a global warming. However, this waste can be friendly in terms of its potential to be used as energy source. HSW into energy conversion technologies has been playing a vital rolein order to successfully address global challenges suchas fossil fuel dependency, emission control and waste management issues.The most promising technology for conversions can be performed using thermochemical processes (e.g., pyrolysis or gasification).These thermochemical technologies can be used to convert solid waste into liquid and gaseous fuels, and this has already been studied sufficiently by other researchers. This article recommends a novel concept for intensification of value-added solid and liquid products recovery from HSW using hydrothermal carbonization and plasma treatment.

The Sustainable Development Goals (SDGs) have addressed environmental and social issues in cities, such as insecure land tenure, climate change, and vulnerability to natural disasters. SDGs have motivated authorities to adopt urban land policies that support the quality and safety of urban life. Reliable, accurate, and up-to-date building information should be provided to develop effective land policies to solve the challenges of urbanization. Creating comprehensive and effective systems for land management in urban areas requires a significant long-term effort. However, some procedures should be undertaken immediately to mitigate the potential negative impacts of urban problems on human life. In developing countries, public records may not reflect the current status of buildings. Thus, implementing an automated and rapid building monitoring system using the potential of high-spatial-resolution satellite images and street views may be ideal for urban areas. This study proposed a two-step automated building stock monitoring mechanism. Our proposed method can identify critical building features, such as the building footprint and the number of floors. In the first step, buildings were automatically detected by using the object-based image analysis (OBIA) method on high-resolution spatial satellite images. In the second step, vertical images of the buildings were collected. Then, the number of the building floors was determined automatically using Google Street View Images (GSVI) via the YOLOv5 algorithm and the kernel density estimation method. The first step of the experiment was applied to the high-resolution images of the Pleiades satellite, which covers three different urban areas in Istanbul. The average accuracy metrics of the OBIA experiment for Area 1, Area 2, and Area 3 were 92.74%, 92.23%, and 92.92%, respectively. The second step of the experiment was applied to the image dataset containing the GSVIs of several buildings in different Istanbul streets. The perspective effect, the presence of more than one building in the photograph, some obstacles around the buildings, and different window sizes caused errors in the floor estimations. For this reason, the operator’s manual interpretation when obtaining SVIs increases the floor estimation accuracy. The proposed algorithm estimates the number of floors at a rate of 79.2% accuracy for the SVIs collected by operator interpretation. Consequently, our methodology can easily be used to monitor and document the critical features of the existing buildings. This approach can support an immediate emergency action plan to reduce the possible losses caused by urban problems. In addition, this method can be utilized to analyze the previous conditions after damage or losses occur.