• Energy Research
• 2021-2025close
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• 6. Clean water close
• 1. No poverty close

In recent years, the rapid development of the rare earth industry has had a serious impact on the environment. Some enterprises have taken measures to improve the production process. In order to explore the sustainability of this industry and these improvements’ environmental benefits, this paper combines emergy analysis and lifecycle assessment to evaluate and compare the production process of rare-earth oxides considering the three aspects of emergy flow, pollutant emissions, and emergy-based indicators. Changes in the emergy of pollutant emissions before and after improvement of the production process are discussed. The results show that the greatest inputs in the mining and beneficiation stage and smelting separation stage are labor force and service and non-renewable resources, respectively. These two production stages are highly dependent on external input and have weak competitiveness. Both stages place great pressure on the environment, so the bastnasite production process would be unsustainable in the long term. After the improvement, the environmental impact of the production process for bastnaesite changed significantly, indicating that the improvement effect of the wastewater treatment facilities and the change of fuel from coal to natural gas is remarkable.

Abstract This study investigates the non-linear relationship between urbanization paths and CO2 emissions in selected South, South-East, and East Asian countries over the period 1971–2014. Based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework, we applied the advanced and robust methods of dynamic seemingly unrelated regression (DSUR), dynamic OLS (DOLS), and fully modified OLS (FMOLS) to estimate the long-term effects. The empirical findings revealed the inverted U-shaped effects of urbanization and urban agglomeration and the U-shaped impact of the largest city ratio on CO2 emissions. Urbanization and urban agglomerations improve environmental quality in the long-run and support ecological modernization theory. However, excessive concentration in the largest cities have severely affected the environmental quality and violates the notion of compact-city efficiencies. Moreover, energy intensity and economic growth positively affect CO2 emissions, while trade openness negatively influences CO2 emissions. Our robustness analysis at the country-level applies the augmented mean group (AMG) panel ARDL technique, which further supports the non-linear effect of urbanization paths on CO2 emissions except for a few countries. The results of the panel Granger non-causality approach unveil bidirectional causality of energy efficiency, economic growth, urbanization, and largest city ratio with CO2 emissions. In contrast, unidirectional causality runs from urban agglomeration to CO2 emissions. Our findings have important policy implications as we suggest green urban infrastructures, eco-friendly dwellings, smart cities, country-specific trade policies, and renewable energy options to improve the environmental quality.

The major goal of this research paper is to determine the long-run linkage among variables and the impact of civil aviation, energy productivity (efficiency), economic growth (GDP), on ecological footprint through conducting the multivariate regression method, Phillips‑Ouliaris and Engle‑Granger, Jarque – Bera Normality, and Cusum tests from 1970 to 2020. According to results of multivariate regression method, civil aviation, energy efficiency, and economic growth affect the ecological footprint from 1970 to 2020 in France which is coincide with Phillips‑Ouliaris and Engle‑Granger tests. On the contrary, there is no effect of independent variables on dependent variable (ecological footprint) in Finland which is consistent with Phillips‑Ouliaris and Engle‑Granger tests. In this respect, The long-run relation of the model is verified by the cointegration test of Engle-Granger and Phillips-Ouliaris for France. However, there is no long-run co-integration among variables for Finland from 1970-2020. To sum up, empirical results of France is verified EKC hypothesis. However, EKC hypothesis is not verified for Finland.

The study assessed the effects of firewood usage on rural women health in Ogun State. Samples of 128 respondents were randomly selected from four local government areas of Ogun State. Data were collected with the aid of structured questionnaire. Percentages were used for data analysis. Majority of the respondents were married (91%) with a mean age of 50 years. Most of the respondents (86.7%) used firewood for cooking, the preference of the utilization of firewood over other alternatives was due to non-availability of other sources (40%) and availability of firewood (33%). Some perceived health effects on the respondents were discomfort caused by the smoke from the firewood (𝑥 ̅ = 2.81) and eyes irritation (= 2.53). The study recommended alternative energy sources that are with lesser health hazards be made readily available at subsidized rate in the rural areas since this was the major attractive driving force for the use of firewood.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

Renewable energy (RE) can play a vital role in ensuring a sustainable energy platform for the future, and in addressing climate change. Investment in RE in the upper middle-income countries (UMICs), which are a major contributor to global carbon dioxide (CO[Formula: see text] emissions, can be an important recovery strategy for mitigating climate change damage. Existing literature suggests that governance (also referred to as ‘institutional quality’) is an important determinant of renewable energy investment (RE investment). Yet, governance conditions in UMICs have remained almost unchanged in the recent past, despite the substantive increases in income levels and investment in the RE sector. This paper outlines an investigation into the role of governance in RE investment in UMICs and considers whether the influence of governance on RE investment depends on a country’s income level. This analysis used pooled ordinary least squares (OLS), panel-corrected standard errors and panel-fixed effects models to evaluate data from 35 UMICs, from 1996 to 2017. The results suggest that good governance raises RE investment in UMICs. In particular, the control of corruption, government effectiveness, regulatory quality and rule of law are statistically significant aspects of good governance that boost RE investment. However, this effect is conditional on the level of economic development (income level) of a country. The marginal effect of governance on RE investment is larger in lower income countries and vice versa. Among the controls, trade openness in UMICs is predicted to have an adverse impact on RE investment.

Real-time pricing demand response programs (RTP-DRPs) are practical measures that ensure the end user's profitability from using electricity by adjusting the supply and demand equilibrium without activating costly solutions. This study explores the potential of RTP-DRPs by developing and applying a region-wise modeling approach based on maximizing the end user's social welfare in the wholesale electricity market in Japan. The regions of the wholesale market are classified based on their response into regions with excess supply, regions with high demand burden, and regular suppliers of inter-regional connections. The results revealed that the RTP-DRPs could potentially reduce the peak demand of the residential sector in Chubu, Chugoku, Kansai, Kyushu, Tokyo, and Tohoku by 1.91%-7.81%. Meanwhile, in Hokkaido, Hokuriku, and Shikoku, by 16.13%-22.9%. The avoided greenhouse emission (GHG) in Tokyo is estimated to be 82.6 and 192.2 tons in summer and winter, respectively.

The present study is motivated by the need to decouple economic growth from environmental degradation given the new wave of chase for higher economic growth trajectories comes with its environmental cost implications, especially among developing blocs like the Emerging 7 (E7) countries. There is a consistent trade-off between economic growth versus environmental quality. Government apparatus are perpetually on the chase for low-carbon emission policies via the pursuit for green economy. To this end, this present study extends the conventional environmental Kuznets curve (EKC) argument by incorporating the role of institution in emerging industrialized economies (E7) and using second-generation panel analysis methods like mean group (MG), augmented mean group (AMG), common correlated effects mean group (CCEMG), and the Dumitrescu and Hurlin causality test for more robust estimates and inferences. To this end, we explore the long-run and causality relationship between economic growth, quadratic form of economic growth, institutional quality, trade flow, investment in energy sector, and financial development in an EKC environment. Empirical analysis established a long-run equilibrium relationship among the outlined variables over the study period. The long-run regression shows the presence of EKC in the E7. Thus, suggesting the preference for GDP growth over environmental quality at the earlier stage of growth curve. Interestingly, investment in energy, trade flow dynamics across the blocs, and financial development dampens the detrimental effect of environmental pollution as we observed negative relationship with the ecological footprint. On the contrary, quality of institution is weak as institutional quality increase (worsen) the quality of environment in the E7 economies. From a policy perspective, this current study proposed the need for more stringent environmental treaties and regulations and promotion of green economy without compromising economic growth. In the conclusion part of the study, more details and specifics about the policy blueprint are presented.