When assessing energy efficiency, most studies have frequently ignored environmental aspects even though the concept has been widely used in the past. This study evaluates the energy efficiency and environmental performance of South Asia by using DEA (data envelopment analysis) like mathematical composite indicator. We construct a comprehensive set of indicators, including an energy self-sufficiency ratio, energy production over consumption ratio, energy imports, diversification index of energy imports, energy reserve ratio, GDP productivity, energy intensity, per capita energy consumption index, carbon emission index, carbon emission index per unit of energy consumption and share of renewable energy in order to develop an energy efficiency and environmental performance index. Unlike other studies, this study first examines each indicator and then estimates a combined score for each country. The results reveal that Bhutan as a more secure country and Pakistan showed a decreasing trend, while Sri Lanka and India performed satisfactorily. Remarkably, Bangladesh, Nepal and Afghanistan showed a decreasing trend. This study proposes a policy that increases the cross-border trade of renewable energy for long term energy efficiency and environmental performance.

The evaluation and prioritization of environmental, social, and governance (ESG) factors are critical for green finance investment strategies. However, ESG criteria are complex and varied concepts that call for a systematic and reliable ranking system to handle ambiguity and uncertainty in decision-makers’ preferences and assessments. The objective of this study was to examine and prioritize environmental, social, and governance (ESG) factors and investment strategies for the development of green finance. Although ESG criteria have gained importance recently, some research gaps still need to be filled. For this purpose, evaluating ESG criteria and integrating them with green finance investment strategies is imperative. This study employed the fuzzy analytical hierarchy process (AHP) method to assess and rank ESG criteria and sub-criteria and the fuzzy weighted aggregated sum product assessment (WASPAS) method to assess and prioritize the key investment strategies for the development of green finance. According to the fuzzy AHP findings, governance and social factors are secondary to environmental considerations in the creation of green finance. Green bonds, ESG integration, and renewable energy funds are essential to green finance methods, according to the fuzzy WASPAS data. This research provides information on creating sustainable and ethical investment strategies for green finance and successfully including ESG factors in investment decision-making processes.

The world is facing the observable and direct impacts of climate change in the shape of extreme weather events, irregular precipitation, glacial melt, sea-level rise, an asymmetric average temperature trends, higher oceanic temperatures, damage to natural ecosystems, and biodiversity. In 2015, collective wisdom stirred the global community to agree upon two unprecedented milestones in recent human history—the Paris agreement and sustainable development goals (SDGs). The twin global agreements set a platform for collective efforts to secure socio-economic and climatic sustainability. Pakistan also joined the board to put forth policy interventions for the mitigation and adaption of climate change risks to maintain symmetry between the environmental quality and economic development. The government of Pakistan (GOP) formulated the National Climate Change Policy (NCCP (2012)) and passed the Pakistan Climate Change Act (PCCA (2017)) to provide an impetus for the implementation of the NCCP. Yet, an appropriate assessment and prioritization of the objectives of the NCCP are indispensable for productive and effective policies to achieve the targets. This study serves this purpose by prioritizing the climate change policy objectives (CCPOs) based on symmetric principles, i.e., energy, transport, urban and town planning, industry, and agriculture as criteria, with 17 sub-criteria in total. The fuzzy analytical hierarchy process (AHP) results show that the energy sector is the most pivotal while implementing the NCCP. Energy production is the most critical area that needs mitigation policy intervention for shifting the energy mix of the economy from high-carbon to low-carbon energy. The fuzzy AHP analysis further revealed the railway, population and urbanization, air pollution, crop growing practices and techniques as the most important related to transport, urban planning, industry, and agriculture sub-criteria, respectively. The fuzzy VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) analysis showed the ranking of the CCPOs from high to low through the integration of policies, institutional capacity building, water security, natural resource management, natural disaster management, environmental financial structure development, and social sector development. The findings of the present research would be helpful for experts and policymakers to re-examine the NCCP and put forth action plans to achieve sustainable climate and SDGs.

Nowadays, green supply chain management (SCM) practices are increasing among firms to adopt green practices and reduce the negative effects of supply chain operations on the environment. Firms such as manufacturing, mining, and agriculture have to improve their capacity in green SCM practices because environmental regulations force them to consider these issues. However, green practices are new and require comprehensive study to determine this problem. This study has taken the case of three garment manufacturing firms for the evaluation of green SCM practices in the context of Pakistan. The green SCM requires multi-dimensional techniques; therefore, fuzzy-based multi-criteria decision analysis approaches must be adopted while assessing green SCM practices of firms. This is because fuzzy-based methods obtain a significant solution for complex, vague, and uncertain multi-attribute problems in fuzzy environment. Therefore, in this study, a hybrid decision model comprised of Delphi, and Fuzzy Analytical Hierarchy Process (AHP) methodologies is proposed for assessing the green SCM practices of firms in terms of green design, green purchasing, green production, green warehousing, green logistics, and reverse logistics. The Fuzzy AHP method results reveal that “green purchasing,” “green design,” and “green production” are ranked the most important green indicators. Further, results reveal the ranking of manufacturing firms (alternatives) in the context of green SCM practices. This study shall help industries to focus on green SCM practices and adopt the green manufacturing process.

Globally, organizations are under enormous pressure to implement green supply chain processes due to growing environmental concerns. Subsequently, organizations and firms have become more conscious of their suppliers’ green innovation ability. However, the selection of the most optimum supplier concerning green innovation ability remains a challenging task that needs to be analyzed. Thus, this study develops an integrated fuzzy and grey-based methodology to analyze and prioritize suppliers for small and medium enterprises (SMEs) in the context of Saudi Arabia. Initially, the study identifies 4 criteria and 20 sub-criteria through extensive literature review with respect to suppliers’ green innovation ability. Later, the Fuzzy Analytical Hierarchy Process (AHP) computes weights of criteria and sub-criteria. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)-Grey was employed to rank the suppliers. The process of assigning weights to criteria and sub-criteria involved twelve experts from academics and industry. The results of Fuzzy AHP indicated that the “Green Innovation Initiatives” is the most significant criterion for the supplier selection. The results of TOPSIS-Grey revealed that the “Supplier-3” is the most optimum supplier having the highest potential of adopting green practices among other suppliers. The overall results provide adequate feedback for organizations and firms to maximize their ability to curb environmental impacts from their upstream activities.

In the ever-evolving technological landscape, ensuring high system availability has become a paramount concern. This research paper focuses on cloud computing, a domain witnessing exponential growth and emerging as a critical use case for high-availability systems. To fulfil the criteria, many services in cloud infrastructures should be combined, relying on the user’s demands. Central to this study is load balancing, an integral element in harnessing the full potential of heterogeneous computing systems. In cloud environments, dynamic management of load balancing is crucial. This study explores how virtual machines can effectively remap resources in response to fluctuating loads dynamically, optimizing overall network performance. The core of this research involves an in-depth analysis of several metaheuristic algorithms applied to load balancing in cloud computing. These include Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony, and Grey Wolf Optimization. Utilizing CloudAnalyst, the study conducts a comparative analysis of these techniques, focusing on key performance metrics such as Total Response Time (TRT) and Data Center Processing Time (DCPT). The findings of this research offer insights into the varying behaviors of these algorithms under different cloud configurations and user retention levels. The ultimate aim is to pave the way for developing innovative load-balancing strategies in cloud computing. By providing a comprehensive evaluation of existing metaheuristic methods, this paper contributes to advancing high-availability systems, underscoring the importance of tailored solutions in the dynamic realm of cloud technology.