Using a multi-level perspective approach combined with top-down macroeconomic models, we analyze the situation of the GCC countries in the perspective of a global transition to zero-net emissions before the end of the century. Based on these analyses, we propose strategic and political options for these oil and gas exporting countries. We show that it would be unwise for GCC member states to adopt an obstructionist strategy in international climate negotiations. On the contrary, these countries could be proactive in developing international emissions trading market and exploiting negative emissions obtained from CO direct reduction technologies, in particular direct air capture with CO sequestration, and thus contribute to a global net-zero-emissions regime in which clean fossil fuels are still used.

This study aims to adapt the Soil and Watershed Assessment Tool (SWAT), a ubiquitously used watershed model, for ground-water dominated surface waterbodies by accounting for recharge from the aquifers. Using measured flow to a headwater slope wetland in Alabama's coastal plain region as a case study, we present challenges and relatively simple approaches in using the SWAT model to predict flows from the draining watershed and relatively simple approaches to model groundwater upwelling. SWAT-simulated flow at the study watershed was limited by precipitation, and consequently, simulated flows were several times smaller in magnitude than observed flows. Thus, our first approach involved a separate stormflow and baseflow calibration which included the use of a regression relationship between observed and simulated baseflow ( = 0.67). Our next approach involved adapting SWAT to simulate upwelling groundwater discharge instead of deep aquifer losses by constraining the range of deep losses, parameter, to negative values ( = 0.75). Finally, we also investigated the use of artificial neural networks (ANN) in conjunction with SWAT to further improve calibration performance. This approach used SWAT-calibrated flow, evapotranspiration, and precipitation as inputs to ANN ( = 0.88). The methods investigated in this study can be used to navigate similar flow calibration challenges in other groundwater dominant watersheds which can be very useful tool for managers and modelers alike.

We propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a , as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply our criterion to the climate-change context and the probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. Insights from computational geometry facilitate computations considerably and allow for the efficient application of the model in high-dimensional settings.

This study was aimed at investigating the determinants of environmental sustainability in 86 countries from 2007 to 2018. The natural gradient boosting (NGBoost) algorithm was implemented along with five machine learning models to forecast the trends of CO emissions. In addition, the SHapley Additive exPlanation (SHAP) technique was used to interpret the findings and analyze the contribution of the individual factors. The empirical results indicated that the predictions obtained using NGBoost were more accurate than those obtained using other models. The SHAP value exhibited a positive correlation among the amount of CO emissions, economic growth, and opportunity entrepreneurship. A negative correlation was observed among the governance, personnel freedom, education, and pollution.

Environmental issues have become a major concern for policymakers faced with the threat of global warming. The European Climate Energy Package is an ambitious plan which drives the trajectories of European countries in three directions: reducing greenhouse gas emissions, increasing the share of renewable energy and improving energy efficiency. This article is original in that it considers the three targets together using multidimensional data analysis methods, a methodology which makes it possible to propose temporal and spatial typologies for the energy transition of European countries over the period 2000-2019. Results show evidence of a gradual transition over three sub-periods towards a more environmentally conscious economy. Four distinct types of energy transition profiles are identified, highlighting the contrasting performances of EU Members in terms of energy transition. In particular, some economically more advanced countries, namely Germany, Ireland, Belgium, Luxembourg and the Netherlands, are lagging in achieving their targets. Finally, discriminant analyses suggest that , , and have been particularly effective in promoting energy transition over the period 2000-2019, while only helps to explain the contrasting results observed at country level over that time.

The traditional meaning of ecological efficiency generally considers only the ratio of economic output to environmental input. This paper expands the meaning and the evaluation system of ecological efficiency from the perspective of improving people's livelihoods. Not only are the discharge of wastewater, waste gas, and solid waste included in the undesired output, but the output index also takes full account of the overall development of the economy, innovation, society and the environment from the perspective of high-quality development. Under the assumption of variable returns to scale, a super-efficiency slack-based measure model based on the undesirable output and Malmquist index is introduced to measure the spatial and temporal variation of ecological efficiency of Zhejiang Province in China, and the panel Tobit method is used to study the key factors affecting ecological efficiency. The results include the four following findings: (1) In the past 12 years, the ecological efficiency of Zhejiang Province has steadily increased, except in 2019 and 2020, when seven cities in Zhejiang Province experienced a decline or near stagnation due to the impact of the economic slowdown and the COVID-19 epidemic. (2) The ecological efficiency of Zhejiang demonstrates a severe regional imbalance, showing a high level in the northeast and a low level in the southwest. (3) Malmquist index analysis shows that the improvement of ecological efficiency in Zhejiang Province has shifted from mainly relying on the dual drivers of pure technical efficiency and scale efficiency in the early stage to relying on technological progress in the later stage. (4) Tobit regression analysis shows that industrialization structure, Theil index, and traffic activity have a significant positive effect on ecological efficiency.

The conventional convexity assumptions frequently placed on piecewise linear frontiers of production technologies modeled using data envelopment analysis imply non-increasing marginal products. Assuming geometric convexity in the context of the exponential technology represents a more general alternative that imposes no underlying restrictions on the marginal products, while simultaneously reducing the impact of the outlying observations. In this paper, we propose an exponential by-production technology capable of generating the outputs deemed undesirable from the society's point of view. We subsequently rely on this technology to measure environmental productivity. Our empirical illustration uses data from the Chinese industrial sector, which is both a major energy consumer and polluter. By comparing our findings with the results from a conventional production model we demonstrate that our proposed indicator mitigates the impact of outlying observations when gauging the contributions of inputs and outputs to green growth. Our results suggest that the Chinese industrial sector experienced the annual productivity growth rate of around 0.40% during 1999-2016 and that the green productivity was mostly driven by technological progress. We also demonstrate that technological progress has been a bigger contributor to the growth in industrial output in China's east than its inland or western regions.

This paper presents a new mathematical model of the green closed-loop supply chain network (GCLSCN) during the COVID-19 pandemic. The suggested model can explain the trade-offs between environmental (minimizing CO emissions) and economic (minimizing total costs) aspects during the COVID-19 outbreak. Considering the guidelines for hygiene during the outbreak helps us design a new sustainable hygiene supply chain (SC). This model is sensitive to the cost structure. The cost includes two parts: the normal cost without considering the coronavirus pandemic and the cost with considering coronavirus. The economic novelty aspect of this paper is the hygiene costs. It includes disinfection and sanitizer costs, personal protective equipment (PPE) costs, COVID-19 tests, education, medicines, vaccines, and vaccination costs. This paper presents a multi-objective mixed-integer programming (MOMIP) problem for designing a GCLSCN during the pandemic. The optimization procedure uses the scalarization approach, namely the weighted sum method (WSM). The computational optimization process is conducted through Lingo software. Due to the recency of the COVID-19 pandemic, there are still many research gaps. Our contributions to this research are as follows: (i) designed a model of the green supply chain (GSC) and showed the better trade-offs between economic and environmental aspects during the COVID-19 pandemic and lockdowns, (ii) designed the hygiene supply chain, (iii) proposed the new indicators of economic aspects during the COVID-19 outbreak, and (iv) have found the positive (reducing CO emissions) and negative (increase in costs) impacts of COVID-19 and lockdowns. Therefore, this study designed a new hygiene model to fill this gap for the COVID-19 condition disaster. The findings of the proposed network illustrate the SC has become greener during the COVID-19 pandemic. The total cost of the network was increased during the COVID-19 pandemic, but the lockdowns had direct positive effects on emissions and air quality.