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Improving green productivity (GP) is critical for achieving global sustainable green development. Yet, past efficiency studies on the African continent have ignored Africa's GP, estimation, and driving factors. Specifically, the role of sovereign debt vulnerability, environmental entrepreneurship, and institutional quality in ensuring Africa's green productivity development is lacking.

Identifying the carbon emission characteristics, driving factors, and decoupling status of the industrial subsectors is important for developing effective policy measures. This allows for implementing industrial emission reduction that, eventually, decouple carbon emission and economic growth. Such an analysis is especially important for the case of China on its way towards sustainable development and increasing global interrelationships

Transition towards low-carbon and carbon-neutral society requires multiple technological solutions. Thermal power generation is the main source of power generation in China and contribute to a large portion of carbon emission. Under the goal of “carbon peak”, thermal power generation efficiency will become an important factor affecting industrial development.

Evaluation of green development is crucial for policy analysis and decision-making. To address the research gap on green development assessment systems, this study first presents the Super-efficiency Bounded Adjusted Measure model to gage the green growth index and economic growth index. This analysis involves evaluation of the coupling coordination degree. Through the analysis of the coordination between green growth and economic growth leads to recommendations for each region under consideration in regards to promotion of the components of the sustainable growth. This study presents the model that melds the objectives of the green and economic growth and uses the results for constructing the sustainable development Lorenz curve and Gini coefficient for the analysis of the differences in the sustainable development levels across the regions. The empirical case is presented to show the possibilities for application of the proposed approach.

Urban and regional development is indispensable from energy systems that function in a sustainable and resilient manner. The generation of renewable energy has seen signifcant upturn in the last decade. Renewables can be exploited to meet the global energy needs and climate change mitigation goals. Therefore, development of urban and regional energy systems must take into account the possibilities and challenges posed by the increasing penetration of the renewable.

Vehicle re-identification technology plays a pivotal role in intelligent transportation systems, contributing significantly to improved traffic scheduling and reduced net-zero emissions. It holds immense potential for advancing carbon neutrality efforts.

Inclusive green growth has received significant attention globally since being proposed in 2012, with an emphasis on the social inclusion and green growth of the economy. In the process of growing the inclusive green economy, innovation has become the key factor in balancing the connection between economic growth, social inclusion, and environmental greenness.

Disruptive events may be critical to supply chains as they may not be viable enough to sustain the challenges. Public support is often offered in order to increase the viability of supply chains. This is particularly important in the context of agri-food supply chains that are crucial for food security. This article proposes a novel framework for the assessment of agri-food products’ supply chain viability during various disruptive external effects.

In recent years, there has been a burgeoning emphasis on advancing the paradigm of energy transformation and expediting the maturation of novel energy systems in order to enhance the structural integrity of China's energy industry.

Through their consumption behavior, households are responsible for more than 70% of total global greenhouse gas emissions. Therefore, the GHG emission reduction potential due to the household behavior is very high. Energy consumption is the main source of the GHG emission in households.