Institutional quality,financial development and sustainable economic growth among lower income countries

This article postulates strong endogenous relationships in lower income countries between institutional quality, financial development and sustained economic growth. These associations were investigated using the vector-error correction model (VECM) and Granger causality method for a sample of 79 countries from 2005 to 2022. The findings show that (1) these variables reinforce each other in the short run. (2) In the long run, both institutional quality and financial development can fuel economic growth. (3) The positive effect of institutional quality on economic growth is greater than that of financial development. Policy implications of these findings are that careful attention should be paid to co-development policies to enhance the institutional quality and the financial system in these economies. Policies should also consider economic growth strategies to enable sustainable economic growth rates.     

Potential energy production from organic waste and its environmental and economic impacts at a tertiary institution in Palestine

Municipal solid waste (MSW) is one of the most well-known biomass resources that can be utilized to produce renewable energy. Numerous countries are plagued by the proliferation of waste, particularly organic waste that can be utilized for energy recovery. Palestine suffers from inefficient solid waste management, and only recently have a few projects focused on bioenergy production been implemented. Throughout the years, the city of Tulkarm experiences power outages which cause a challenge to the Palestine Technical University-Kadoorie campus in Tulkarm. Thus, the possibility of energy recovery from the organic portion in Palestine Technical University-Kadoorie was evaluated. The analysis of an economic impact included discussions of a number of economic aspects, including Levelized cost of energy, internal rate of return, present worth, annual worth, and payback period. On the other hand, a carbon dioxide savings analysis and gas emission were evaluated. The outcomes of the energy optimization demonstrated that the suggested system could supply the institution with an average of roughly 7 MWh of electrical energy. According to the economic study, this project offers 0.25 million dollars in present value, 0.144 million dollars in annual value, a 13 percent internal rate of return, a payback period of 6 years, and a levelized cost of energy of 0.11 dollars for each kWh generated. Additionally, the environmental assessment revealed that this system might reduce CO₂ emissions by around 8,343,778 tons. For effective waste management, energy recovery, and emission reduction, it is advised to implement anaerobic digestion technology.     

Toward sustainable use of natural resources: Nexus between resource rents,affluence, energy intensity and carbon emissions in developing and transition economies

Sustainable use of natural resources would entail ensuring that derived economic benefits today do not undermine the welfare of generations to come. On this basis, this study examines the nexus between natural resource rents and carbon dioxide (CO₂) emissions disaggregated into production and consumption-based (i.e., trade-adjusted) CO₂ emissions for a selected panel of 45 developing and transition economies over the period 1995–2017. The empirical model also incorporates the impacts of population, affluence, and energy intensity. The results show that affluence increases production-based CO₂ emissions by 1.407%, with the EKC's predicted inverted U-shaped curve only explaining consumption-based CO₂ emissions. Economic reliance on natural resource rents and energy intensification contribute 0.022% and 0.766%, respectively, to CO₂ emissions embedded in territorial production inventories and 0.035% and 0.583%, respectively, to CO₂ emissions embedded in consumption inventories. The bootstrap non-causality test shows that historical data on each variable has significant predictive power for future CO₂ emissions from both sources. The historical information about natural resource rents has significant predictive power over the future levels of affluence and energy intensity. Clearly, the results show that the environmental impact of natural resource rents is stronger when CO₂ emissions are adjusted for trade and varies among the countries, with Bangladesh, Guinea, India, Malaysia, Mexico, Nigeria, Pakistan, Saudi Arabia, Vietnam, and Zimbabwe among the most affected countries. Overall, this study provides motivation for policies to keep the use of natural resources within sustainable limits.     

Lithium extraction from salt lake brines with high magnesium/lithium ratio: a review

Environmental Chemistry Letters - The demand for lithium is growing rapidly with the increase in electric vehicles, batteries and electronic equipments. Lithium can be extracted from brines, yet...     

A multidecadal oscillation in precipitation and temperature series is pronounced in low flow series from Puget Sound streams

In Pacific Northwest streams, summer low flows limit water available to competing instream (salmon) and out-of-stream (human) uses, creating broad interest in how and why low flows are trending. Analyses that assumed linear (monotonic) change over the last ~60 years revealed declining low flow trends in minimally disturbed streams. Here, polynomials were used to model flow trends between 1929 and 2015. A multidecadal oscillation was observed in flows, which increased initially from the 1930s until the 1950s, declined until the 1990s, and then increased again. A similar oscillation was detected in precipitation series, and opposing oscillations in surface temperature, Pacific Decadal Oscillation, and Interdecadal Pacific Oscillation series. Multidecadal oscillations with similar periods to those described here are well known in climate indices. Fitted model terms were consistent with flow trends being influenced by at least two drivers, one oscillating and the other monotonic. Anthropogenic warming is a candidate driver for the monotonic decline, and variation in (internal) climatic circulation for the oscillating trend, but others were not ruled out. The recent upturn in streamflows suggests that anthropogenic warming has not been the dominant factor driving streamflow trends, at least until 2015. Climate projections based on simulations that omit drivers of multidecadal variation are likely to underestimate the range, and rate of change, of future climatic variation.     

How does eliminating mercury from artisanal and small-scale gold mining lead to achieving sustainable development goals?

Artisanal and small-scale gold mining (ASGM) is the principal anthropogenic activity that globally contributes to overloading our environment with mercury. Although the Minamata Convention, led by the United Nations, is a crucial instrument to eliminate its use progressively, novel approaches to accelerate this difficult transition are welcome. This article proposes a framework for policy-making or improvement, fostering the enforcement of mercury elimination through the lens of the 17 Sustainable Development Goals (SDGs), focusing on the excluded artisanal and small-scale gold miners and their dependents. We move forward with a literature review of the Artisanal and Small Mining topic, taking each SDG as a unit of analysis. Understanding the problem as a puzzle of four sets of pieces, namely: (1) social, (2) environmental, (3) economic, and (4) institutional, the paper offers potential opportunities for the decision-makers and practitioners to accelerate the substitution of this heavy metal and develop sustainable futures for the ASGM communities. We conclude by proposing a pragmatic framework that synthesizes the means, actions, and ends to accelerate a sustainable transition.     

Investigation of long-term river water quality variations using different urbanization indices and assessment of common scientific perspectives of urbanization on water quality

This study investigated the water quality variation spanning 30 years (1986–2017) in 16 catchments of Hong Kong against different urbanization indices, namely, built area fraction; population; and product of population and built area fraction. Pearson correlations of three different periods of time (1988–1990, 1998–2000, and 2015–2017) indicated that water quality trends were dependent on the urbanization index. Total solids, nitrite-nitrogen, total phosphorus, electrical conductivity, dissolved oxygen, and flow rate had significant deteriorative trends (Pearson r > 0.5 and p < 0.05) with population and product of built area and population. Results also interpreted that built area fraction and product of built area and population were the worst and best indices that represented urbanization and/or its impacts, respectively. Mann-Kendall test for the entire 30 year period showed that water quality had improved with time with respect to certain water quality parameters (e.g., dissolved oxygen, ammoniacal nitrogen and total suspended solids). The results portrayed that although the urbanization of catchments had increased with time, the river water quality with respect to many parameters showed signs of improvement and the legislative measures implemented seemed to be effective in controlling pollution.     

Inexpensive substrate sampler for macroinvertebrates

Assessment of aquatic macroinvertebrates is a critical component of many watershed monitoring programs and passive samplers are often used to collect long-term site data, especially in environments where active sampling is not possible. However, standard passive samplers can be expensive and lost in extreme conditions. We developed a sampler using plastic soda bottles (PSB) filled with river rock and compared its effectiveness with standard Hester-Dendy samplers in both lotic and lentic environments. Abundance, taxa richness, and macroinvertebrate composition showed no significant differences between sampler types in either habitat type. PSB samplers, which can be constructed for less than one dollar each, collected the same number of organisms and represented the same diversity as Hester-Dendy devices that cost around $38 each. In studies where funds are limited, PSB samplers appear to be suitable for passive monitoring.     

Sulfonated graphene nanomaterials for membrane antifouling,pollutant removal,and production of chemicals from biomass: a review

Water pollution and the unsustainable use of fossil fuel derivatives require advanced catalytic methods to clean waters and to produce fine chemicals from modern biomass. Classical homogeneous catalysts such as sulfuric, phosphoric, and hydrochloric acid are highly corrosive and non-recyclable, whereas heterogeneous catalysts appear promising for lignocellulosic waste depolymerization, pollutant degradation, and membrane antifouling. Here, we review the use of sulfonated graphene and sulfonated graphene oxide nanomaterials for improving membranes, pollutant adsorption and degradation, depolymerization of lignocellulosic waste, liquefaction of biomass, and production of fine chemicals. We also discuss the economy of oil production from biomass. Sulfonated graphene and sulfonated graphene oxide display an unusual large theoretical specific surface area of 2630 m²/g, allowing the reactants to easily enter the internal surface of graphene nanosheets and to reach active acid sites. Sulfonated graphene oxide is hydrophobic and has hydrophilic groups, such as hydroxyl, carboxyl, and epoxy, thus creating cavities on the graphene nanosheet’s surface. The adsorption capacity approached 2.3–2.4 mmol per gram for naphthalene and 1-naphthol. Concerning membranes, we observe an improvement of hydrophilicity, salt rejection, water flux, antifouling properties, and pollutant removal. The nanomaterials can be reused several times without losing catalytic activity due to the high stability originating from the stable carbon–sulfur bond between graphene and the sulfonic group.

     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.