• Energy Research
• 1. No poverty close
• PK close
• SA close
• BE close

Energy and electricity are critical prerequisites for every nation and critical components of social and economic growth. The monetary policy economist has been debating the issue of relying on core inflation or headline inflation, which includes energy, because the energy price is so volatile that it obscures the trend in inflation and dilutes the objectives of monetary policy. This paper aims at analyzing the impact of the twin deficit on energy inflation within a linear and nonlinear framework in India using time series data covering the period from 1971 to 2021. ARDL and NARDL approaches are employed to investigate the linear/symmetric response of energy inflation due to the twin-deficit in India. Estimates show a negative relationship between the twin-deficit and energy inflation in a symmetric model. The results of the asymmetric model reveal that the response of energy inflation due to expansionary fiscal policy (increment in CFD) and contractionary fiscal policy (decline in CFD) is diverse, in terms of size of the coefficient. Further, estimates of NARDL show the distinct impact of increases and decreases in CAD on energy inflation. The study offers several implications for policymakers and energy economists.

Based on contingency and agency insights, this study examines the influence of ownership characteristics on performance measurement systems (PMSs) and outcome-based compensation systems driven by differences in organizational goals and objectives between state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs) in Vietnam. The influence of ownership characteristics on the design of PMSs received little attention from researchers so far. Moreover, the few studies that are available so far only examined the relationship between firm ownership characteristics and the presence and use of economic performance indicators and economic outcome-based compensation in firms. In this study, the scope of PMSs is broader, and sustainability indicators focusing on community programs, ethical behavior, and government regulation are included in addition to economic based indicators. Analyzing survey data with the use of partial least squares (PLS) structural equation modeling (SEM), we find that the higher the share of the government in an organization’s capital is, the significantly more governmental duty indicators and significantly fewer ethical indicators and economic indicators are included in the PMS and outcome-based compensation systems. The inclusion of community indicators is not associated with firm ownership characteristics. Meanwhile, non-SOEs include significantly more economic value indicators, but no societal measures, like ethical, community-oriented, and governmental duty indicators.

The rapid growth of agriculture has led to a significant increase in energy utilization and CO2 emissions. Agriculture performs a pivotal role in improving every country’s economy. The current study has the main objective to analyses the long-run impact of agricultural CO2 discharge on economic growth, energy consumption (electricity utilization in the agriculture sector), financial development, foreign direct investment (FDI), and population for India. For the period 1978–2018, we applied ADF, PP, ERS, and KPSS unit root and Z&A and CMR structural interval tests to evaluate the stability and breaks in the data set. We check the cointegration of study variables through ARDL, Engle–Granger, and Johansen’s cointegration approaches. The findings of the long-run analysis showed a cointegration among the variables, which reveals that an escalation in economic growth and financial development refine the natural environment, while the upsurge in FDI and population further deteriorate the climate in India. However, agricultural sector electricity use shows an insignificant association with CO2 emissions for both periods. On the basis of results, we recommend that legislators should offer an environment that provides opportunities for financial development and economic growth.

© 2021 Elsevier LtdDespite extensive research to address the impact of environmental reforms under the Paris Climate Agreement, current literature has failed to provide sufficient insights into Regional Comprehensive Economic Partnership (RCEP) countries. To this end, the current study attempts to address the impact of the economic complexity on environmental quality in the presence of renewable energy consumption, financial development, urbanization and energy innovation in RCEP countries from 1990 to 2019. Our empirical estimates confirm a significant association between environmental quality, economic complexity index, renewable energy consumption, financial development, urbanization and energy innovation in the short-run and long run. Based on extensive econometric analysis (CS-ARDL, AMG, PMG, FMOLS, and DOLS), we conclude that economic complexity, renewable energy, and energy innovation effectively mitigate environmental degradation. At the same time, financial development and urbanization have an adverse impact on the environment. These findings have extensive policy implications for policymakers and environmental stakeholders, who are aiming to achieve sustainable energy policy and economic growth to meet the environmental commitments under Paris Climate Agreement.

Environmental degradation has put significant consequences at both the national and global levels. Economic development and ecological sustainability can be achieved by an increase in economic growth but on the cost of environmental quality. The basic aim of this analysis is to find the existence of the aviation Kuznets curve (AKC) and the environmental Kuznets curve (EKC) for the selected 21 OECD nations from 1980 to 2018. The aviation cargo and aviation passengers’ patterns are separately analyzed in relation to CO2 emissions to quantify whether Kuznets phenomenon exists in the aviation industry of OECD nations? The econometric results of panel GMM confirm the validation of the aviation Kuznets curve in case of aviation passengers such that the “inverted U shaped” pattern. While the environmental Kuznets curve and aviation cargo Kuznets curve do not exist in the OECD nation. It means that an increase in aviation cargo and economic growth increases the CO2 emissions showing the “U shaped” association. Moreover, the Pairwise Dumitrescu Hurlin panel causality test exhibits uni–directional association moving from economic growth to carbon emissions, while there is bidirectional granger causality among trade openness, aviation sector, and energy consumption to carbon emissions. The overall results suggest that OECD nations must consider the pollution reduction techniques, investment in renewable sources, continuous research, implementation of efficient energy sources, and the imposition of aviation emissions taxes to limit the pollution.

The current and projected warming of the earth is unequivocal with humans playing a strong role as both perpetrators and victims. The warming on the African continent is projected to be greater than the global average with an increased average temperature of 3–6°C by the end of the century under a high Representative Concentration Pathway. In Africa, the Sub-Saharan region is identified as the most vulnerable to the changing climate due to its very low capacity to adapt to or mitigate climate change. While it is common to identify studies conducted to assess how climate change independently impacts water, land, or food resources, very limited studies have sought to address the interlinkages, synergies, and trade-offs existing between climate change, water, land, and food (WLF) resources as a system in Sub-Saharan Africa (SSA). The climate change and WLF security nexus, therefore, seeks to address this shortfall in literature and subsequently serve as a relevant source of information for decision-making and policy implementation concerning climate change mitigation and adaptation. In this study, 41 relevant studies were selected from Web of Science, Google Scholar, ResearchGate, and institutional websites. We provide information on the independent relationships between climate change and WLF resources, and further discuss the existing inter-linkages between climate change and the WLF security in SSA using the nexus approach, with recommendations on how decision making and policy implementations should be done using the climate change and WLF security nexus approach.

Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the actual state of food insecurity, expert and consensus-based approaches and surveys are currently used. Both require substantial manpower, time, and budget. This paper introduces an extreme gradient-boosting machine learning model to forecast monthly transitions in the state of food security in Ethiopia, at a spatial granularity of livelihood zones, and for lead times of one to 12 months, using open-source data. The transition in the state of food security, hereafter referred to as predictand, is represented by the Integrated Food Security Phase Classification Data. From 19 categories of datasets, 130 variables were derived and used as predictors of the transition in the state of food security. The predictors represent changes in climate and land, market, conflict, infrastructure, demographics and livelihood zone characteristics. The most relevant predictors are found to be food security history and surface soil moisture. Overall, the model performs best for forecasting Deteriorations and Improvements in the state of food security compared to the baselines. The proposed method performs (F1 macro score) at least twice as well as the best baseline (a dummy classifier) for a Deterioration. The model performs better when forecasting long-term (7 months; F1 macro average = 0.61) compared to short-term (3 months; F1 macro average = 0.51). Combining machine learning, Integrated Phase Classification (IPC) ratings from monitoring systems, and open data can add value to existing consensus-based forecasting approaches as this combination provides longer lead times and more regular updates. Our approach can also be transferred to other countries as most of the data on the predictors are openly available from global data repositories.

Abstract This paper examines the relationship between hydroelectricity consumption and economic growth in China, while controlling for fossil fuel consumption, financial development, capital, institutional quality and globalization and its components for the period, 1970–2014. We have employed the Bayer and Hanck, (2013) combined cointegration test to examine the long-run relationships between those variables as well as the autoregressive distributed lag method with structural breaks as a robustness check. The empirical findings demonstrate a long-run relationship between those variables. Hydroelectricity consumption, fossil fuel consumption, capital, financial development and globalization and its components have a positive influence on GDP in China. The findings also provide predominant evidence on the long-run feedback hypothesis between the variables. The findings suggest that policies should be implemented to increase the role hydropower in the energy mix for sustainable economic growth in the country.