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Technology spillovers in an open economy, a source of innovation, potentially contribute to reducing environmental pollution and improving environmental quality; however, few studies have explored this issue in detail. Based on the heterogeneous industrial R&D expenditure data issued by the OECD structural analysis database and the trade data collected from statistics on international trade by commodity in the OECD, this study evaluates the diversified channels of the import-related technology spillovers in manufacturing industries in China. Moreover, the study empirically investigates the eco-efficiency impact of technology spillovers via decomposed diversified spillover channels. The findings show that import-related technology spillovers positively affect the eco-efficiency of manufacturing industries. Inter-industrial technology spillovers, rather than intra-industrial technology spillovers, contribute more to aggregate technology spillovers and, accordingly, have played a more prominent role in promoting eco-efficiency.

The interconnection of traffic infrastructure and the logistics industry’s efficient operation is a basis for the reconstruction of industrial spatial structure and economic optimization of the urban cluster. In this paper, the Undesirable-SBM DEA model was used to calculate the logistics eco-efficiency (LEE) of the five coastal urban clusters for 2009–2017, and the panel fixed-effect Tobit model was used to analyze the LEE evolution mechanism. Findings show that the overall score for LEE in China’s five coastal urban clusters is in a downward trend, and the gap in LEE between the cities is increasing. The temporal and spatial distribution of the LEE score has evolved from the pyramid structure of “the number of high-efficiency cities is the largest, followed by medium-efficiency in-between, and the least being low-efficiency cities” to the inverted triangle structure of “the number of low-efficiency cities is the largest, followed by medium-efficiency in-between, and the least being high-efficiency cities”. The evolution of the spatial distribution of LEE in various urban clusters shows heterogeneous characteristics. The cities with middle- and high-investment volumes of logistics resources are mainly the core and sub-core cities, but most of them have low LEE scores. The employee excess rate is the main reason for the loss of LEE. Energy prices and government intervention significantly and negatively affect LEE, while environmental regulations positively and significantly affect the excess of capital stock and employees. The impact of influencing factors, such as economic development and environmental regulations, on LEE and input excesses is different among the five coastal urban clusters.

The technologies for building environmentally sound homes are well known and have been demonstrated in numerous projects, but are yet to be adopted as standard in the UK. Local authority development plans now include "sustainability" within their principles, but this is applied mainly in the social context. One barrier to achieving sustainable new housing is the lack of a framework to combine the objectives and policies of the urban environment, environmental impacts, resource use, and the design and construction of the houses themselves. This paper describes a framework designed for use by decision-makers (i.e. housing developers and planning officers) to help them address one part of "sustainability", namely eco-efficiency. The theoretical framework is then applied to a case study. Both the development process and the application of the framework reveal interesting points to be considered in the progress towards sustainable housing.

The analysis of innovation, natural resource consumption, and eco-efficiency is widely discussed in firm-level studies. However, there is little literature on the assessment of eco-efficiency and its determinants in the macroeconomic framework. This study focuses on the empirical estimation of the eco-efficiency index with and without undesirable output at the aggregate level using a non-radial, non-oriented slack-based model (SBM) with the help of data envelopment analysis (DEA). SBM-DEA reveals that the level of eco-efficiency falls by incorporating undesirable output. The system generalised method of moments (GMM) applied to the panel data from 116 developed and developing countries for the period 2010–2019 to identify the determinants of eco-efficiency. The findings of system GMM show that innovation has a dynamic impact on eco-efficiency in developed and developing countries compared to natural resource consumption which adversely impacts it. The study also confirms exports and foreign direct investment (FDI) as an integral determinant of eco-efficiency. The findings confirm the ecological modernization theory which assures improvement in environmental quality due to innovation. The findings suggest that innovation and conservation of natural resources promote eco-efficiency in developed and developing countries.

Gas refineries are an important part of the energy supply in the world but they may produce byproducts like emissions too. Thus, both technical performance and environmental execution are important and need to be analyzed. Iran is the third-highest producer of natural gas in the world. It produces 6.5% of the natural gas in the world. This paper rummages the environmental and technical performance of Iranian gas refineries through data envelopment analysis (DEA), which is a strong frontier analysis approach is used in this study. Although the primary result shows a rather good performance of Iranian gas refineries overall, more potentials are found for improvement. We find 6% technical potential and 8% environmental potential for improvement which is still a considerable amount. Thus, national and international policymakers should consider this potential for better production and supply of natural gas. We also provided an empirical robustness check to enrich our findings.

In this study, we construct a green innovation network by using the gravity model and propose a panel model to analyze the mechanism by which the green innovation network structure of the urban agglomeration affects eco-efficiency and take the Beijing-Tianjin-Hebei urban agglomeration as an example. Results show that the urban agglomeration of Beijing, Tianjin, and Hebei is characterized by radial development, with “Beijing-Tianjin” serving as the core. The central position of city nodes in the innovation network of the Beijing-Tianjin-Hebei urban agglomeration significantly impacts its eco-efficiency, whereas other characteristics do not.