Search Results (180)

Morocco is facing a situation of alarming water stress, aggravated by climate change, overexploitation of resources, and unequal distribution of water, placing the country among the most vulnerable to water scarcity in the MENA region. This study aims to investigate the dynamic relationship between economic growth and water stress in Morocco while highlighting the importance of integrated water management and adaptive economic policies to enhance resilience to water scarcity. A mixed methodology, integrating both qualitative and quantitative methods, was adopted to overview the economic–environmental Moroccan context, and to empirically analyze the GDP (gross domestic product) and water stress in Morocco over the period 1975–2021 using an Autoregressive Distributed Lag (ARDL) approach. The empirical analysis is based on annual data sourced from the World Bank and FAO databases for GDP, agricultural value added, renewable internal freshwater resources, and water productivity. The results suggest that water productivity has a significant positive effect on economic growth, while the impacts of agricultural value added and renewable water resources are less significant and vary depending on the model specification. Diagnostic tests confirm the reliability of the ARDL model; however, the presence of outliers in certain years reflects the influence of exogenous shocks, such as severe droughts or policy changes, on the Moroccan economy. The key contribution of this study lies in the fact that it is the first to analyze the intrinsic link between economic growth and the environmental aspect of water in Morocco. According to our findings, it is imperative to continuously improve water productivity and adopt adaptive management, rooted in science and innovation, in order to ensure water security and support the sustainable economic development of Morocco. Full article

Most existing precipitation data fusion methods rely on reliable precipitation values, such as those observed from ground-based rain gauges, to correct the satellite precipitation estimates (SPEs) that often involve systematic biases. However, such reliable data are rarely available in many regions of the world, especially in rugged terrain and hostile regions, rendering the correction suboptimal. To address this limitation, we propose a novel data fusion method—Triple Collocation Spatial Autoregression under Dirichlet distribution (TCSpAR-Dirichlet)—which eliminates the need for reliable data while still having the capability to effectively capture true precipitation patterns. The key idea in our method is using the variance of the precipitation estimates at each grid location obtained from each satellite to optimally leverage the associated satellite’s weight in data fusion, then characterizing the weights on all locations by a spatial autoregression model, and finally using the fitted weights to fuse the multi-sourced SPEs at all grid locations. We apply this method to SPEs in Nepal, which does not have ground gauges in many of its mountainous areas, to collect reliable precipitation data, to produce a fused precipitation dataset with uniform spatial coverage and high measurement accuracy. Full article

Ensuring the synergistic advancement of agricultural pollution reduction and carbon emission mitigation, along with sustainable development, is crucial for achieving the ‘dual carbon’ target and modernizing agriculture. To ensure sustainable agricultural development, this study employs a coupling coordination model to explore the synergistic effects of pollution reduction and carbon emission mitigation in Henan Province, considering the agricultural carbon emissions (ACEs), agricultural non-point source pollution (ANP), and the gross value of agricultural output (GVAO) of 18 cities in Henan from 2010 to 2022 as endogenous variables. A panel vector autoregression (PVAR) model is utilized to analyze the interactive responses between agricultural pollution reduction and carbon emission mitigation and agricultural economic development. The results indicate that the degree of synergy between ACE and ANP in Henan Province has shown a trend towards higher values and a diminishing polarization phenomenon between 2010 and 2022, with most regions having degrees of synergy at higher levels. Furthermore, the interactive response relationships between agricultural pollution reduction and carbon emission mitigation and agricultural economic development reveals that the GVAO in Henan Province has a significant positive impact on both ACE and ANP, and that agricultural pollution reduction and carbon emission mitigation are constrained by agricultural economic development, with no significant bidirectional causal relationship observed overall and a lack of positive interaction in the long term. Finally, ACE, ANP, and GVAO in Henan Province exhibit a strong self-reinforcing mechanism, particularly ACE and GVAO, which show a pronounced self-growth trend. Overall, Henan Province should fully utilize the synergistic effects of agricultural pollution reduction and carbon emission mitigation to achieve coordinated progress in agricultural pollution reduction and carbon emission mitigation, as well as green and sustainable development of the agricultural economy. Full article

Ecologically sustainable economic development is increasingly recognized as essential to global efforts to improve and protect environmental and socio-economic conditions. The transportation sector is also important regarding the movement of human beings and goods. Fossil fuels are primarily used in transport vehicles and emit carbon dioxide into the atmosphere. Hence, innovative investments in the transportation system and the use of renewable energy play a key role in overcoming this lingering problem. This study utilizes nonlinear autoregressive distributed lag (NARDL) methods to uncover key drivers influencing innovative investments in the transportation sector and the impact of renewable energy use on environmental sustainability in France between 1995 and 2020. The results indicate that renewable energy use and transport infrastructure innovations positively and negatively impact environmental sustainability. Both variables have different influences on the dependent variable depending on the economic shock period. Based on the outcomes, this study offers the following significant policy insights: (i) France could invest in innovations in renewable energy sourcing and incentivize switching from combustion engine-based transport systems. (ii) France should commit to the Europe 2020 strategy for green growth to ensure resource efficiency and promote environmental sustainability, which requires a coordinated effort to invest in smart transport systems that leverage technologies like the Internet of Things, artificial intelligence, and big data analytics. (iii) Given that two-thirds of France’s electricity is produced from nuclear sources, the government needs to implement policies in the renewable energy sector to reduce over-reliance on nuclear energy sourcing. Full article

Mountain regions are particularly sensitive and vulnerable to the impacts of climate change. Over the past three decades, mountain temperatures have risen significantly faster than those in lowland areas. The Hindu Kush–Karakoram–Himalaya region, often referred to as the “water tower of Asia”, is the largest freshwater source outside the polar regions. However, it is currently undergoing cryospheric degradation as a result of climatic change. In this study, the Normalized Difference Glacier Index (NDGI) was calculated using Landsat and Sentinel satellite images. The results revealed that glaciers in Chitral, located in the Eastern Hindu Kush Mountains of Pakistan, lost 816 km² (31%) of their total area between 1992 and 2022. On average, 27 km² of glacier area was lost annually, with recession accelerating between 1997 and 2002 and again after 2007. Satellite analyses also indicated a significant increase in both maximum (+7.3 °C) and minimum (+3.6 °C) land surface temperatures between 1992 and 2022. Climate data analyses using the Mann–Kendall test, Theil–Sen Slope method, and the Autoregressive Integrated Moving Average (ARIMA) model showed a clear increase in air temperatures from 1967 to 2022, particularly during the summer months (June, July, and August). This warming trend is expected to continue until at least 2042. Over the same period, annual precipitation decreased, primarily due to reduced snowfall in winter. However, rainfall may have slightly increased during the summer months, further accelerating glacial melting. Additionally, the snowmelt season began consistently earlier. While initial glacier melting may temporarily boost water resources, it also poses risks to communities and economies, particularly through more frequent and larger floods. Over time, the remaining smaller glaciers will contribute only a fraction of the former runoff, leading to potential water stress. As such, monitoring glaciers, climate change, and runoff patterns is critical for sustainable water management and strengthening resilience in the region. Full article

Corporate taxation assumes a pivotal role in all economies, as it constitutes a substantial source of revenue for governmental agencies tasked with fulfilling social obligations. Nonetheless, modifications in financial policies and the unpredictability of macroeconomic factors result in a significant decline in this vital revenue source for the government. This study examines the financial determinants influencing corporate tax revenue in Nigeria from 1990 to 2022. In this analysis, the broad money supply, access to credit by the private sector, borrowing costs, and exchange rates are utilized as independent variables, while corporate tax revenue serves as the dependent variable. Data pertinent to this investigation on corporate income tax are sourced from the Federal Inland Revenue Service, whereas information regarding the broad money supply and credit extended to the private sector is acquired from the Central Bank of Nigeria. Additionally, statistical data on interest and exchange rates are gathered from the World Bank. This investigation applies autoregressive distributed lag and error correction models, acknowledging the existence of a long-term relationship within the series. The significant findings indicate that the broad money supply positively and significantly affects corporate income tax in the short run, but this effect diminishes to a positively insignificant level in the long run. Additionally, the interest rate is shown to have a significant harmful effect on corporate tax income in the short run, while it becomes negatively insignificant over the long term. Other financial policy factors do not significantly account for changes in corporate income tax. This study suggests the formulation of financial policies that are advantageous to corporate organizations, particularly through the reduction in borrowing costs, to facilitate business growth and enhance the government’s ability to collect substantial corporate tax revenue. The originality of this research is apparent in its utilization of financial policy instruments to illustrate the effectiveness of financial guidelines on corporate tax receipts and to argue for particular amendments that are essential when these guidelines prove detrimental to business activities. Full article

Inflation rate is considered undesirable in the modern globalized world due to its adverse and long-lasting impacts. The Kingdom of Saudi Arabia (KSA, hereafter) has also experienced inflationary pressure during the last few years, specifically post-COVID-19. However, the empirical literature on the determinants of inflation is indeed very scarce in the context of KSA. Amid this backdrop, this research paper aims to figure out the true determinants of inflation by focusing on the role of supply chain disruptions and global uncertainty by focusing on KSA. Quantitative data were collected from credible sources on a monthly basis for the period of 1998M01 to 2024M02 and were analyzed through the “Autoregressive Distributed Lag Model (ARDL)”. Our findings indicate that inflation in KSA is positively impacted by supply chain disruptions, global uncertainty, inflation spillovers from the United States, and money supply in the long run. Similarly, in the short run, only money supply, supply chain disruptions, and global uncertainty are responsible for the prevailing inflation rate in KSA. Moreover, the real effective exchange rate is positively and significantly linked with inflation only in the long run. Furthermore, positive shocks in oil prices cure inflation, while negative shocks in oil prices accelerate inflation in the short run. Our results are expected to shape policy formulation regarding the management of the inflation rate in KSA significantly. Full article

Analyzing the influence of external shocks on the pricing dynamics of the live pig industry chain is essential for effective macroeconomic control. Utilizing monthly data spanning from January 2010 to August 2023, this study employs the TVP-SV-VAR (Time-Varying Parameter—Stochastic Volatility—Vector Autoregression) model to analyze the effects of EPU (Economic Policy Uncertainty) and INU (Live Pig Industry News Uncertainty) on industry pricing. The findings are as follows: Firstly, the impacts of EPU and INU on industry prices exhibit time variability and distinct characteristics. Specifically, the impact magnitude of EPU ranges between [−0.025, 0.025], and that of INU between [−0.01, 0.01]. These differences in impact magnitude elicit varied responses from manufacturers and consumers to the indices. Secondly, uncertainty shocks at particular time points show high consistency, suggesting a patterned influence of external shocks on industry pricing that aligns with historical trends. Thirdly, robustness tests with alternative explanatory variables confirm the reliability of the findings. An uncertainty index, crafted from more comprehensive information sources, more accurately captures the effects of external shocks on industry pricing. Additionally, the volume of live pig slaughters illustrates the potential interaction between external shocks and pricing dynamics. In an era marked by increasingly frequent external shocks, this research offers valuable insights for policymakers to implement macro-control and foster high-quality industrial development. Full article

Geothermal resources are considered a clean energy source, and their development plays a key role in achieving sustainable development. This energy contributes to environmental protection, energy security, and economic growth, while also helping to alleviate energy poverty. Qinghai Province, rich in geothermal resources, holds significant potential for development. First, this study evaluated the geothermal resources in the uplifted mountainous regions of Qinghai Province using the volumetric method and analyzed their spatial distribution. Next, the degree of geothermal resource endowment was measured, and the relationship between geothermal resources and industrial structure was analyzed. Finally, the Vector Autoregression (VAR) model and impulse response function were applied to assess the impact and duration of geothermal resources on changes in the industrial structure from 2000 to 2020. Geothermal resources in Qinghai Province exhibit significant regional variation, with the northern and western regions being particularly rich in geothermal resources, peaking at 3.58 × 10¹⁷ J in Banma County. Geothermal resources in Qinghai are predominantly utilized for power generation, averaging 42.20% of energy consumption. The interplay between geothermal resource use and industrial structure is intensifying, notably in secondary and tertiary sectors. Initially restrictive, the influence of industrial structure on geothermal resource use is projected to become facilitative as clean energy technologies advance. This study revealed the relationship between geothermal resources and the local industrial structure in Qinghai Province, providing a scientific basis for the sustainable and efficient development and utilization of these resources. It contributed to the long-term sustainability of geothermal resource exploitation. Full article

Emerging countries are the main source of new CO₂ emissions and the major net carbon importers, and they have also become an important part of the global trade pattern. In this study, the impact of trade openness on CO₂ emissions was investigated by approaches such as fully modified least squares (FMOLS), dynamic ordinary least squares (DOLS), and pooled mean group-autoregressive distributive lag (PMG-ARDL) methods. Further estimations were conducted by employing methods such as DCCEMG (dynamic common-correlated effect mean group) and Driscoll–Kray to strengthen the robustness of the results. Moreover, the Granger causality between trade openness and CO₂ emissions was tested by using the Dumitrescu–Hurlin method. Conclusions can be drawn as follows: First, economic growth, energy consumption, trade openness, and CO₂ emissions are all interconnected in the long term. Specifically, higher levels of economic growth and trade openness are associated with lower CO₂ emissions, whereas energy consumption contributes to higher emissions. However, in the short term, economic growth and energy consumption lead to an increase in CO₂ emissions, while trade openness does not have a significant impact. Moreover, there is a two-way Granger causality between trade openness and CO₂ emissions. Additionally, economic growth and energy consumption have an indirect effect on CO₂ emissions by influencing trade openness. Given these findings, emerging market countries should focus on enhancing their service sectors, promoting technological advancements, and fostering international collaboration in green technologies. By actively engaging in efforts to combat climate change, these countries reach a point where trade expansion and carbon reduction are achieved. Full article

Biomass is the primary source of renewable energy in Poland. Its share in renewable energy production in Poland has decreased in recent years, but it still maintains a nearly 70% share. Poland has extensive forest and straw resources, such as pellets, which can be used for stable biomass production. The main objective of this research was to understand the potential of plant biomass production for energy purposes in Poland and other European Union (EU) countries in terms of sustainable development. The period of analysis covered 2000–2022. Secondary data from Statistical Poland and Eurostat were used. The primary research method was the Augmented Dickey–Fuller (ADF) test, which aimed to check the stationarity of stable biomass. Moreover, we calculated the Vector Auto-Regressive (VAR) model, which was used to develop the forecast. The indigenous production of solid biomass in 2022 decreased to 363,195 TJ, while in 2018, it was 384,914 TJ. Our prognosis confirms that biomass will increase. The prognosis based on the VAR model shows an increase from 365,395 TJ in 2023 to 379,795 (TJ) in 2032. Such countries as France, Germany, Italy, Spain, Sweden, and Finland have a bigger potential for solid biomass production from forests because of their higher area. As a result, Poland’s biomass production competitiveness is varied when compared to other EU nations; it is lower for nations with a large forest share and greater for those with a low forest cover. The two main benefits of producing solid biomass are its easy storage and carbon dioxide (CO₂) neutrality. The main advantage is that solid biomass preserves biodiversity, maintains soil fertility, and improves soil quality while lowering greenhouse gas emissions and environmental pollutants. The ability to leave added value locally and generate new jobs, particularly in troubled areas, is the largest social advantage of sustained biomass production. Full article

Coastal aquifers are critical freshwater resources that face increasing threats from contamination and saltwater intrusion. Traditional approaches for characterizing these aquifers are challenged by complex dynamics, high-dimensional parameter spaces, and significant computational demands. This study presents an innovative method that combines an Auto-Regressive Convolutional Neural Network (AR-CNN) surrogate model with the Iterative Local Updating Ensemble Smoother (ILUES) for the joint inversion of contamination source parameters and hydraulic conductivity fields. The AR-CNN surrogate model, trained on synthetic data generated by the SEAWAT model, effectively approximates the complex input–output relationships of coastal aquifer systems, substantially reducing computational burden. The ILUES framework utilizes observational data to iteratively update model parameters. A case study involving a heterogeneous coastal aquifer with multipoint pollution sources demonstrates the efficacy of the proposed method. The results indicate that AR-CNN-ILUES successfully estimates pollution source strengths and characterizes the hydraulic conductivity field, although some limitations are observed in areas with sparse monitoring points and complex geological structures. Compared to the traditional SEAWAT-ILUES framework, the AR-CNN-ILUES approach reduces the total inversion time from approximately 70.4 h to 16.2 h, improving computational efficiency by about 77%. These findings highlight the potential of the AR-CNN-ILUES framework as a promising tool for efficient and accurate characterization of coastal aquifers. By enhancing computational efficiency without significantly compromising accuracy, this method offers a viable solution for the sustainable management and protection of coastal groundwater resources. Full article

The unpredictable nature of renewable energy sources, such as wind and solar, makes them unreliable sources of energy for the power system. Nevertheless, with the advancement in the field of artificial intelligence (AI), one can predict the availability of solar and wind energy in the short, medium, and long term with fairly high accuracy. As such, this research work aims to develop a machine-learning-based framework for forecasting global horizontal irradiance (GHI) for Muscat, Oman. The proposed framework includes a data preprocessing stage, where the missing entries in the acquired data are imputed using the mean value imputation method. Afterward, data scaling is carried out to avoid the overfitting/underfitting of the model. Features such as the GHI cloudy sky index, the GHI clear sky index, global normal irradiance (GNI) for a cloudy sky, GNI for a clear sky, direct normal irradiance (DNI) for a cloudy sky, and DNI for a clear sky are extracted. After analyzing the correlation between the abovementioned features, model training and the testing procedure are initiated. In this research, different models, named Linear Regression (LR), Support Vector Machine (SVR), KNN Regressor, Decision Forest Regressor, XGBoost Regressor, Neural Network (NN), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor, Categorical Boosting (CatBoost), Deep Autoregressive (DeepAR), and Facebook Prophet, are trained and tested under both identical features and a training–testing ratio. The model evaluation metrics used in this study include the mean absolute error (MAE), the root mean squared error (RMSE), R², and mean bias deviation (MBD). Based on the outcomes of this study, it is concluded that the Facebook Prophet model outperforms all of the other utilized conventional machine learning models, with MAE, RMSE, and R² values of 9.876, 18.762, and 0.991 for the cloudy conditions and 11.613, 19.951 and 0.988 for the clean weather conditions, respectively. The mentioned error values are the lowest among all of the studied models, which makes Facebook Prophet the most accurate solar irradiance forecasting model for Muscat, Oman. Full article

Energy is not only the crucial driver of economic activities within rural areas. Conventional energy sources are crucial for the prosperity of rural areas; however, they also prove detrimental to the rural ecosystem. To achieve sustainable rural development, increasing the consumption of renewable energy sources can prove vital. Among all the renewable energy sources, bioenergy is the cheapest and easiest to produce in rural areas. Therefore, it is crucial to estimate the impact of bioenergy on the rural development of China. Thus, the primary purpose of this analysis is to analyze the impact of bioenergy and environmental policy stringency on the rural development of China from 1995 to 2022 by employing the autoregressive distributed lag (ARDL) and quantile autoregressive distributed lag (QARDL) framework. The results highlight the significance of bio-energy for rural development in the short and long run. Likewise, environmental policy stringency is also a vital factor in fostering short- and long-run rural development. Based on these outcomes, it is recommended that policymakers integrate bioenergy development policies into broader rural development strategies. Full article

The power generation by solar photovoltaic (PV) systems will become an important and reliable source in the future. Therefore, this aspect has received great attention from researchers, who have investigated accurate and credible models to predict the power output of PV modules. This prediction is very important in the planning of short-term resources, the management of energy distribution, and the operation security for PV systems. This paper aims to explore the sensitivity of Nonlinear Autoregressive Exogenous Inputs (NARX) and an Artificial Neural Network (ANNs) as a result of weather dynamics in the very short term for predicting the power output of PV modules. This goal was achieved based on an experimental dataset for the power output of a PV module obtained during the sunny days in summer and cloudy days in winter, and using the data in the algorithm models of NARX and ANN. In addition, the analysis results of the NARX model were compared with those of the static ANN model to measure the accuracy and superiority of the nonlinear model. The results showed that the NARX model offers very good estimates and is efficient in predicting the power output of the PV module in the very short term. Thus, the coefficient of determination (R²) and mean square error (MSE) were 94.4–97.9% and 0.08261–0.04613, respectively, during the summer days, and the R² and MSE were 90.1–89.2% and 0.281–0.249, respectively, during the winter days. Overall, it can be concluded that the sensitivity of the NARX model is more accurate in the summer days than the winter days, when the weather conditions are more stable with a gradual change. Moreover, the effectiveness of the NARX model has the specificity to learn and to generalize more effectively than the static ANN. Full article