Natural resources and economic performance: Evaluating the role of political risk and renewable energy consumption

Resources Policy, Год журнала: 2022, Номер 78, С. 102890 - 102890

Опубликована: Июль 12, 2022

Язык: Английский

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Neutralizing the surging emissions amidst natural resource dependence, eco-innovation, and green energy in G7 countries: Insights for global environmental sustainability

Journal of Environmental Management, Год журнала: 2023, Номер 344, С. 118560 - 118560

Опубликована: Июль 7, 2023

Язык: Английский

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Can renewable energy technology innovation promote mineral resources’ green utilization efficiency? Novel insights from regional development inequality

Resources Policy, Год журнала: 2023, Номер 82, С. 103449 - 103449

Опубликована: Март 14, 2023

Язык: Английский

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Analyzing the co-movement between CO2 emissions and disaggregated nonrenewable and renewable energy consumption in BRICS: evidence through the lens of wavelet coherence

Environmental Science and Pollution Research, Год журнала: 2023, Номер 30(13), С. 38921 - 38938

Опубликована: Янв. 1, 2023

Язык: Английский

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Return connectedness and multiscale spillovers across clean energy indices and grain commodity markets around COVID-19 crisis

Journal of Environmental Management, Год журнала: 2023, Номер 340, С. 117912 - 117912

Опубликована: Апрель 22, 2023

Язык: Английский

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Effects of climate change and anthropogenic activity on the vegetation greening in the Liaohe River Basin of northeastern China

Ecological Indicators, Год журнала: 2023, Номер 148, С. 110105 - 110105

Опубликована: Март 8, 2023

Elucidating the response mechanism of variation in vegetation trend to determinant is great value environmental resource management, particularly significant ecologically fragile area. The Liaohe River Basin (LRB) a key part eco-security China, which has experienced apparent climatic variations and intensified human activities recent decades. Yet, it still remains not clear about drivers shaping spatio-temporal patterns growth. Here, normalized difference index (NDVI) was utilized investigate coverage from 2000 2019. Then, we incorporated partial derivatives analysis conduct attribution analyses greening light meteorological data. prime findings are as follows: (1) LRB presented growing state 20 years at rate 0.0031/a, with spatial temporal heterogeneity due its slope; (2) results showed that average contribution precipitation, temperature, solar radiation NDVI changes 0.00205/a, 0.00008/a, −0.00028/a, respectively. (3) change played most dominant role influencing result relative contributions 59.68% (40.32% contributed by anthropogenic activities); (4) LULC dynamics were characterized an increase forest land large-scale ecological afforestation projects, coverage. Conversely, urbanization adversely affected variations. Understanding this study expected offer further scientific support practical implications for monitoring local status.

Язык: Английский

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Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102498 - 102498

Опубликована: Янв. 26, 2024

Land Use and Cover (LULC) maps are vital prerequisites for weather prediction models. This study proposes a framework to generate LULC based on the U.S. Geological Survey (USGS) 24-category scheme using Google Earth Engine. To realize precise map, fusion of pixel-based object-based classification strategies was implemented various machine learning techniques across different seasons. For this purpose, feature importance analysis conducted top classifiers considering dynamic (seasonal) behavior LULC. The results showed that ensemble approaches such as Random Forest Gradient Tree Boosting outperformed other algorithms. also demonstrated approach had better performance due consideration contextual features. Finally, proposed produced map with higher accuracy (overall = 94.92% kappa coefficient 94.19%). Furthermore, generated assessed by applying it Weather Research Forecasting (WRF) model downscaling wind speed 2-m air temperature (T2). assessment indicated effectively reflected real-world conditions, thereby impacting estimation T2 fields WRF. Statistical assessments enhancements in RMSE 0.02 °C, MAE 1 Bias 0.03 °C T2. Additionally, there an improvement 0.06 m/s speed. Consequently, can be produce accurate up-to-date high-resolution geographical areas worldwide. source codes corresponding research paper available GitHub via https://github.com/Mganjirad/GEE-LULC-WRF.

Язык: Английский

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Natural resources and sustainable development: Evaluating the role of remittances and energy resources efficiency

Resources Policy, Год журнала: 2022, Номер 80, С. 103214 - 103214

Опубликована: Дек. 9, 2022

Язык: Английский

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Disaggregating the impact of natural resource rents on environmental sustainability in the MENA region: A quantile regression analysis

Resources Policy, Год журнала: 2023, Номер 85, С. 103825 - 103825

Опубликована: Июнь 17, 2023

Язык: Английский

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Can agricultural digital transformation help farmers increase income? An empirical study based on thousands of farmers in Hubei Province

Environment Development and Sustainability, Год журнала: 2023, Номер 26(6), С. 14405 - 14431

Опубликована: Апрель 17, 2023

Язык: Английский

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