Evolving waste management: The impact of environmental technology, taxes, and carbon emissions on incineration in EU countries.

Amid the urgent global imperatives concerning climate change and resource preservation, our research delves into the critical domains of waste management and environmental sustainability within the European Union (EU), collecting data from 1990 to 2022. The Method of Moments Quantile Regression (MMQR) results reveal a resounding commitment among EU member states to diminish their reliance on incineration, which is evident through adopting green technologies and environmentally conscious taxation policies, aligning with the European Union's sustainability objectives. However, this transition presents the intricate task of harmonizing industrial emissions management with efficient waste disposal. Tailoring waste management strategies to accommodate diverse consumption patterns and unique circumstances within individual member states becomes imperative. Cointegrating regressions highlighted the long-run relationship among the selected variables, while Feasible Generalized Least Squares (FGLS) and Panel-Corrected Standard Errors (PCSE) estimates roughly confirmed MMQR results. ML analyses, conducted through two ensemble methods (Gradient Boosting, GB, and Extreme Gradient Boosting, XGBoost) shed light on the relative importance of the predictors: in particular, environmental taxation, consumption-based emissions, and production-based emissions greatly contribute to determining the variation of combustible renewables and waste. This study recommends that EU countries establish monitoring mechanisms to advance waste management and environmental sustainability through green technology adoption, enhance environmental taxation policies, and accelerate the renewable energy transition.

Assessing the drivers and solutions of green innovation influencing the adoption of renewable energy technologies.

The degradation of the environment in China is accelerating along with economic expansion. Adoption of renewable energy technologies (RETs) is crucial for reducing the adverse impacts of economic growth on the environment and fostering sustainable development. This study attempts to identify the green innovation drivers and sub-drivers that affect the adoption of RETs in China and provide solutions for boosting their implementation. The study prioritized the drivers, sub-drivers, and strategies of green innovation by combining the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. In the study, the triple bottom line (TBL) approach has been used to determine the economic, societal, and environmental driving forces. The study also suggests strategies for encouraging the use of RETs. The results of the AHP method revealed that economics is the most crucial driver, with a weight of 0.376, followed by environmental (0.332), and social (0.291) drivers. The findings of the SAW method indicated that government green innovation initiatives, consumer initiatives, and industry initiatives are the most significant strategies for deploying RETs in China. This study has important theoretical and practical ramifications for encouraging China to adopt RETs. The suggested approaches can help researchers, business professionals, and policymakers promote sustainable development in China.

Untapped potential of food waste derived biochar for the removal of heavy metals from wastewater.

The presence of heavy metals in water pose a serious threat to both public and environmental health. However, the advances in the application of low cost biochar based adsorbent synthesize from various feedstocks plays an effective role in the of removal heavy metals from water. This study implies the introduction of novel method of converting food waste (FW) to biochar through pyrolysis, examine its physiochemical characteristics, and investigate its adsorption potential for the removal of heavy metals from water. The results revealed that biochar yield decreased from 18.4 % to 14.31 % with increase in pyrolysis temperature from 350 to 550 °C. Likewise, increase in the pyrolysis temperature also resulted in the increase in the ash content from 39.87 % to 42.05 % thus transforming the biochar into alkaline nature (pH 10.17). The structural and chemical compositions of biochar produced at various temperatures (350, 450, and 550 °C) showed a wide range of mineralogical composition, and changes in the concentration of surface functional groups. Similarly, the adsorption potential showed that all the produced biochar effectively removed the selected heavy metals from wastewater. However a slightly high removal capacity was observed for biochar produced at 550 °C that was credited to the alkaline nature, negatively charged biochar active sites due to O-containing functional groups and swelling behavior. The results also showed that the maximum adsorption was recorded at pH 8 at adsorbent dose of 2.5 g L-1 with the contact time of 120 min. To express the adsorption equilibrium, the results were subjected to Langmuir and Freundlich isotherms and correlation coefficient implies that the adsorption process follows the Freundlich adsorption isotherm. The findings of this study suggest the suitability of the novel FW derived biochar as an effective and low cost adsorbent for the removal of heavy metals form wastewater.

Exploring the determinants of methane emissions from a worldwide perspective using panel data and machine learning analyses.

This article contributes to the scant literature exploring the determinants of methane emissions. A lot is explored considering CO2 emissions, but fewer studies concentrate on the other most long-lived greenhouse gas (GHG), methane which contributes largely to climate change. For the empirical analysis, a large dataset is used considering 192 countries with data ranging from 1960 up to 2022 and considering a wide set of determinants (total central government debt, domestic credit to the private sector, exports of goods and services, GDP per capita, total unemployment, renewable energy consumption, urban population, Gini Index, and Voice and Accountability). Panel Quantile Regression (PQR) estimates show a non-negligible statistical effect of all the selected variables (except for the Gini Index) over the distribution's quantiles. Moreover, the Simple Regression Tree (SRT) model allows us to observe that the losing countries, located in the poorest world regions, abundant in natural resources, are those expected to curb methane emissions. For that, public interventions like digitalization, green education, green financing, ensuring the increase in Voice and Accountability, and green jobs, would lead losers to be positioned in the winner's rankings and would ensure an effective fight against climate change.