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AbstractEfficient allocation of resources and transition towards cleaner sources of energy are among the key factors in minimizing the negative externalities arising from the growing dependency on fossil fuels. Moreover, environmentally benign trade policies are crucial due to the rapid expansion in global trade and comparative advantage. To explore the relationship between global trade and Consumption‐Based Carbon Emissions (CBCE) in China in the context of renewable energy consumption and energy efficiency from 1990 Q1 to 2020 Q4, this paper employs the dynamic Autoregressive Distributed Lags (Dynamic ARDL) model to evaluate the connection between international trade, renewable energy usage, energy efficiency, and CBCE, with the Kernel‐Based Regularized Least Squares analysis being used to determine the causal association. The findings reveal that GDP and imports are directly linked with CBCE in the long run, while exports, energy efficiency, and renewable energy consumption have a negative significant long‐run and short‐run influence on CBCE. In addition, exports help in the reduction of CBCE by sharing and adapting environmentally friendly technologies adopted in, for example, renewable energy and ecological advancements. Renewable energy promotes the quality of the environment, which is attributable to the performance of renewable energy and environmentally friendly technologies that directly stimulate the reduction of CBCE emissions. By cutting CBCE, energy efficiency tends to boost environmental quality. The research findings have noteworthy policy implications; trade enhances the quality of environment by introducing lower energy intensive production methods and technology that support sustainable development. As a result, this study advocated quite stringent policies in terms of aligning international trade orientation with environmental quality policy in China. Furthermore, energy efficiency policies are required to lower CBCE by focusing on high‐energy‐using industries and providing alternative clean energy sources.

This paper presents a methodical framework, which is the basis for the economic analysis of the mid-term planning of development of the Polish energy system. The description of the partial equilibrium model and its results are demonstrated for different scenarios applied. The model predicts the generation, investment and pricing of mid-term decisions that refer to the Polish electricity and heat markets. The current structure of the Polish energy sector is characterised by interactions between the supply and demand sides of the energy sector. The supply side regards possibilities to deliver fuels from domestic and import sources and their conversion through transformation processes. Public power plants, public CHP plants, industry CHP plants and municipal heat plants represent the main producers of energy in Poland. Demand is characterised by the major energy consumers, i.e. industry and construction, transport, agriculture, trade and services, individual consumers and export. The relationships between the domestic electricity and heat markets are modelled taking into account external costs estimates. The volume and structure of energy production, electricity and heat prices, emissions, external costs and social welfare of different scenarios are presented. Results of the model demonstrate that the internalisation of external costs through the increase in energy prices implies significant improvement in social welfare.

Abstract This paper examines the existence of an inverted U-shaped relationship between income and CO2 emissions from fossil fuels for a panel of 161 countries over the period 1992–2012. Our contribution, empirical in nature, is threefold. (i) We estimate the Environmental Kuznets Curve for CO2 emissions using three conventional measures of GDP taken from the PWT and the WDI and show that the results of the estimates are unstable and that the turning points are dependent on the measure of GDP. (ii) We use a new dataset of GDP based on the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light imagery as a proxy for the development level, and using these data, which are less prone to the measurement errors in the System of National Accounts statistics, we find evidence for the existence of an Environmental Kuznets Curve for CO2 emissions. (iii) We apply a U test that confirms that the estimated extreme point is within our data range. The evaluated turning point, beyond which CO2 emissions start to fall as income rises, is considerably lower than earlier studies show, and thus, our results provide more optimistic prospects of the possible environmental benefits of economic growth.

Abstract Energy poverty is a recognized issue on the EU political agenda, yet relatively little is known about its dynamics. This paper provides new evidence on the profiles of energy-poor households in Poland and the determinants that allow them to escape energy poverty. Our study is the first to assess the persistence of energy poverty in Poland; it examines long-term interactions between energy poverty and poverty. The analysis is based on panel data obtained from the 2014 to 2017 waves of the EU Survey on Income and Living Conditions and uses subjective indicators of energy poverty and poverty. The results suggest that energy poverty in Poland is a transitory state which the poor are less likely to escape. Two distinct energy-poverty profiles are identified in the study: 1) middle-aged inhabitants of rural areas who are mostly inactive on the labor market and live in single-family houses; 2) elderly pensioners from urban areas, mostly women who live in old blocks of flats. The determinants of escaping energy poverty are cluster-specific. The findings suggest that energy poverty reduction should be targeted within renovation policy, employment policy, and social assistance.

Abstract The aim of this study is to assess the effect of renewable energy sector development on the relationships between electricity consumption and economic growth in the European Union countries in the period between 1995 and 2015. A panel vector autoregressive model is used for testing Granger causality and estimating impulse-response functions. The results indicate that the relationships between economic growth and electricity consumption depend on the level of renewable energy sector development. The greater the share of the renewable energy sector in the economy is, the more noticeable the mutual dependence between the economic growth and renewable electricity consumption is. In countries with relatively well-developed renewable energy sectors renewable electricity consumption boosts the economy and vice versa. In the remaining countries economic growth and electricity consumption are independent. Empirical findings suggest that policymakers should develop strategies to implement clean energy policy aimed at stimulating the development of the renewable energy sector to support economic growth.

Since bettering environmental conditions has acquired significant interest globally, discovering factors that may facilitate the establishment of environmental sustainability is currently of foremost importance. Hence, this study considers a sample of 33 members of the Organization for Economic Cooperation and Development and checks whether reducing exposure to different forms of country risks, in the presence of international trade and clean energy consumption, can reduce their respective carbon footprint levels. Utilizing annual data from 2000 to 2018 and employing methods that handle problems related to dependence across cross-sectional units and heterogeneity of slope coefficients, the findings endorse that (a) reducing financial and political risks abate carbon footprints, (b) economic risk exposure does not influence carbon footprints, (c) international trade exerts carbon footprint-boosting effects, and (d) undergoing unclean to clean energy transition curbs carbon footprints. Accordingly, the concerned governments should these findings into account while conceptualizing green environmental policies in the future.

SummaryPhosphorus (P) is a major agricultural nutrient and, in its mineable form, a potentially scarce resource. Countries with limited physical access to P should hence develop an effective national P governance. This requires analyses of trends and variations in P flows and stocks over time. Here, we present a long‐term P flow analysis for the Indian agri‐food sector from 1988 to 2011. Major P flows are imports of mineral P, fertilizer application, and uptake of animal fodder. The mineral P import dependency ratio is constant at around 93%. On average, 20% of P inputs to soils are lost through erosion. Key drivers of changes in P flows include population growth, dietary change, and agricultural intensification. To reduce its P fertilizer import dependence, India could, for example, substitute up to 19% of the presently applied mineral P if manure used as a household fuel were recycled, and up to 21% if P was fully recovered from wastewater and household waste. Comparing selected indicators for P use in agriculture with China and the European Union (EU) reveals that there are structural similarities, such as increasing fertilizer application rates and P accumulation in soils, with the first but large differences compared to the latter. The analyses highlight that in contrast to static indicators, the time‐continuous tracking of P flows provides substantial advantages, such as the identification of long‐term trends, drivers, and intervention options for sustainable P management, given that it allows for the interpretation of present indicators in the context of past trends and legacies.

Abstract Renewable energy should play a crucial role in increasing energy supplies and achieving the potential target of reducing 50% of CO2 emissions by 2050. The main objective of this study is to propose a neuro-fuzzy modelling entitled ensemble-Adaptive Neuro-Fuzzy Inference System (ANFIS) learning to predict and analyse the interrelationship between renewable energy consumption, economic growth, and CO2 emissions of G8+5 countries. This will help the governments and industry sectors to formulate energy policies and develop energy resources sustainably. The prediction method was constructed by extracting the fuzzy rules from the real-world dataset of World Development Indicators (WDI) and generalising the relationships of the inputs and output parameters for accurate prediction of CO2 emissions. The performance of the proposed method was evaluated, and the results show its efficiency in the prediction of CO2 emissions by incorporating the import indicators, including renewable energy consumption and economic growth. The U test of Sasabuchi–Lind–Mehlum (SLM) was conducted to identify the interrelationship results obtained from the ensemble ANFIS learning and the Environmental Kuznets Curve (EKC) hypothesis. The results of SLM test found an inverse U-shape condition among all countries except Brazil. The prediction of CO2 emissions trends using the soft computing approach (ensemble ANFIS) indicated that the consumption of renewable energy reduces CO2 emissions. The proposed soft computing method was found efficient in predicting CO2 emissions. It was in line with the foreseen targets of increasing the renewable energy generation and achieving the nationally determined contributions (NDCs) objectives.

Abstract This paper presents a novel multi-period stochastic optimization model for studying long-term power generation capacity planning in developing countries. A stylized model is developed to achieve three objectives: (1) to serve as a tool for determining optimal mix, size and timing of power generation types in the face of budget constraint, (2) to help decision makers appreciate the consequences of capacity expansion decisions on level of unserved electricity demand and its attendant impact on the national economy, and (3) to encourage the habit of periodic savings towards new generation capacity financing. The problem is modeled using a stochastic mixed-integer linear programming (MILP) technique under demand uncertainty. The effectiveness of the model, together with valuable insights derived from considering different levels of budget constraints are demonstrated using Ghana as a case study. The results indicate that at an annual savings equivalent to 0.75% of GDP, Ghana could finance the needed generation capacity to meet approximately 95% of its annual electricity demand between 2016 and 2035. Additionally, it is observed that as financial constraint becomes tighter, decisions on the mix of new generation capacities tend to be more costly compared to when sufficient funds are available.