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Production-related industrial zones, super structures and infrastructures are constructed by the construction industry. Nearly all industries and their environmental emissions are influenced by the construction industry including its sub-industries, companies and their supply chains. Furthermore, cities play an important role in economic growth. Cities are hubs for productivity, production, supply and demand, and innovation with the help of their human capital and built environment (e.g. offices, factories, industrial zones, infrastructures, etc.).

Industrial growth fosters urbanisation which is vital for the supply side in the economy to reach to the human resources. Urbanisation which supports industrial growth obstacles industries’ efficiency due to urbanisation problems (e.g. traffic, air and water pollution, health problems).

Construction industry and its sub-industries affect total factor productivity growth in nearly all industries. Construction industry can be a facilitator industry for economic growth and industrial growth considering total factor productivity growth and environment aspects. All industries’ green and sustainable total factor productivity growth can be supported by rethinking construction industry, its sub-industries and their outputs (e.g. construction materials, built environment, cities) as well as construction project management processes.

This chapter aims to introduce carbon capturing smart construction industry model to foster green and sustainable total factor productivity growth of industries. This chapter emphasises current and potential roles of construction industry, its sub-industries and their outputs in fostering other industries’ growth through green and sustainable total factor productivity growth. It focusses on carbon capturing technologies and design at different levels. Furthermore, this chapter emphasises cities’ role in green and sustainable total factor productivity growth. This chapter provides recommendations for construction industry policies and carbon capturing cities/built environment model to solve urbanisation problems and to foster industrial growth and green and sustainable total factor productivity growth. This chapter is expected to be useful to all stakeholders of the construction industry, policy makers, and researchers in the relevant field.

This study aims to explain the integration of innovation and climate with the economic growth Green Productivity (GP) concept. This is drawn from the integration of two important developmental strategies: productivity improvement and environmental protection. Productivity provides the framework for continuous improvement, while environmental protection provides the foundation for sustainable development. Therefore, GP is a strategy for enhancing productivity and environmental performance for overall socio-economic development.

Three variations of frameworks and econometric model were developed to measure green total factor productivity, green labour productivity and green capital productivity, and their contributions to green productivity and sustainable development; these were based on extensive and intensive growth theories.

The sustainability of higher economic growth will likely continue to be productivity driven. This will be through the enhancement of total factor productivity (TFP) as technological progress in nations that combined the three dimensions of sustainable development (economic development, environmental protection and social sustainable development via human capital development). Such an enhancement needs to emphasise the quality of the workforce, demand intensity, economic restructuring, capital structure, technical progress and environmental standards. It should be recalled that green productivity through green TFP demonstrates the sustainable development concept of progressing technologically. It will ensure the rights of the future, as well as current, generations for them to enjoy a better life.

The study fills the gaps in growth theories by developing three variations of frameworks and econometric models, and internalising pollutants emissions as private and unpriced inputs in the three models. Further, the green capital productivity model is the sole contributing model developed in this research; it has not been thought about in any previous studies. This study highlighted the green productivity that is ignored by the studies that have been awarded the Nobel Prize in economic sciences in 2018.

Through this study, the aim is to provide reference for the improvement of CEO related theories, the reform of internal governance mechanisms and compensation systems in enterprises, and ultimately contribute to economic and social development and the achievement of dual carbon goals.

In the context of accelerating the implementation of the “dual carbon” goal and promoting sustainable economic and social development, this paper builds a panel data model based on the panel data of 31 provinces in China from 2011 to 2019 to empirically test the impact of CEO stability on green innovation and total factor productivity of enterprises.

The results show that CEO stability has a positive role in promoting enterprise total factor productivity and green innovation and enterprise green innovation also has a positive role in promoting total factor productivity; Heterogeneity testing found that CEO stability has no significant impact on green innovation in state-owned enterprises. Corporate green innovation has a partial mesomeric effect between CEO stability and total factor productivity; The proportion of independent directors has a negative moderating effect on CEO stability and corporate green innovation, while equity incentive has a positive moderating effect on the relationship between CEO stability, corporate total factor productivity and green innovation.

First, it develops the research on CEO stability and its economic consequences. Second, it expands the impact of CEO stability on heterogeneous enterprises. Third, provides new ideas for the reform of internal governance mechanism and salary system of enterprises.

The aim of this article is to research on forestry green total factor productivity and explore the impact of financial support on forestry green total factor productivity.

The methods used in this study are super efficiency SBM model of undesired output and empirical model. SBM model is a kind of Data Envelopment Analysis (DEA). The SBM model with non-expected outputs (slacks-based measure) can be used to deal with the problem of efficiency measurement with multiple input and output variables and can be used to analyze the efficiency of green development of forestry economy.

First, the overall green total factor productivity of the authors’ country's forestry has shown a trend of first decline and then an increase from 2008 to 2018, and there are significant spatiotemporal differences; second, financial support has a significant positive impact on forestry green total factor productivity; third, environmental regulation has a significant threshold effect in the process of financial support on forestry green total factor productivity, and the role of financial support shows a trend of first increasing and then decreasing.

Secondly, taking the data of 30 provinces and cities in the authors’ country from 2008 to 2018 as the research object, using the super-efficiency SBM-Malmquist index to measure the country's forestry green total factor productivity and analyze its temporal and spatial changes; finally, a dynamic panel model was established to explore the impact of financial support on forestry green total factors quantitative impact on productivity, and adding environmental regulation as a threshold variable to establish a dynamic threshold regression, and found that financial support has a nonlinear impact on forestry green total factor productivity.

Agricultural carbon productivity considers the dual goals of “agricultural economic growth” and “carbon emission reduction”. Improving agricultural carbon productivity is a requirement for promoting green and low-carbon development of agriculture. Agricultural production agglomeration is widespread worldwide, but the relationship between agricultural production agglomeration and agricultural carbon productivity is inconclusive. This paper aims to study the impact of agricultural production agglomeration on agricultural carbon productivity, which is conducive to a better understanding of the relationships among agglomeration, agricultural economic development and carbon emission, better planning of agricultural layout to build a modern agricultural industrial system and achieve the goal of carbon peaking and carbon neutrality.

Based on China's provincial data from 1991 to 2019, this paper uses non-radial directional distance function (NDDF) and Metafrontier Malmquist–Luenberger (MML) productivity index to measure total factor agricultural carbon productivity. Subsequently, using a panel two-way fixed effect model to study the effect and mechanism of agricultural production agglomeration on agricultural carbon productivity, and the two-stage least squares method (IV-2SLS) is used to solve endogeneity. Finally, this paper formulates a moderating effect model from the perspective of the efficiency of agricultural material capital inputs.

The empirical results identify that Chinese provincial agricultural carbon productivity has an overall growth trend and agricultural technological progress is the major source of growth. There is an inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity. The input efficiency of agricultural film, machine and water resources have moderating effects on the inverted U-shaped relationship. Agricultural production agglomeration also promotes agricultural carbon productivity by inhibiting agricultural carbon emissions in addition to affecting agricultural input factors and its internal mechanisms are agricultural green technology progress and rural human capital improvement.

This paper innovatively adopts the NDDF–MML method to measure the total factor agricultural carbon productivity more scientifically and accurately and solves the problems of ignoring group heterogeneity and the shortcomings of traditional productivity measurement in previous studies. This paper also explains the inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity theoretically and empirically. Furthermore, from the perspective of agricultural material capital input efficiency, this paper discusses the moderating effect of input efficiency of fertilizers, pesticides, agricultural film, agricultural machines and water resources on agricultural production agglomeration affecting agricultural carbon productivity and answers the mechanism of carbon emission reduction of agricultural production agglomeration.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

The purpose of this study is to examine the spatial fluctuation spillover effect of green total factor productivity (GTFP) under the influence of Internet development.

Using panel data from 283 cities in China for the period 2003–2016, this paper explores the spatial fluctuation spillover effect of internet development on GTFP by applying the spatial autoregressive with autoregressive conditional heteroscedasticity model (SARspARCH).

The results of Moran's I test of the residual term and the Bayesian information criterion (BIC) value indicate that the GTFP has a spatial fluctuation spillover effect, and the estimated results of the SARspARCH model are more accurate than the spatial autoregressive (SAR) model and the spatial autoregressive conditional heteroscedasticity (spARCH) model. Specifically, the internet development had a positive spatial fluctuation spillover effect on GTFP in 2003, 2011, 2012 and 2014, and the volatility spillover effect weakens the positive spillover effect of internet development on GTFP. Moreover, Internet development has a significant positive spatial fluctuation spillover effect on GTFP averagely in eastern China and internet-based cities.

The results of this study provide digital solutions for policymakers in improving the level of GTFP in China, with more emphasis on regional synergistic governance to ensure growth.

This paper expands the research ideas for spatial econometric models and provides a more valuable reference for China to achieve green development.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.