Reducing the effect of mankind’s activities on the climate and improving adaptability to global warming have become urgent matters. The carbon footprint (CF), derived from the concept of ecological footprint, has been used to assess the threat of climate change in recent years. As a “top to bottom” method, input–output analysis (IOA) has become a universally applicable CF assessment tool for tracing the carbon footprint embodied in economic activities. A wide range of CF studies from the perspective of the IOA model have been presented and have made great progress. It is crucial to have a better understanding of what the relevant research focuses on in this field, yet so far a systematic synopsis of the literature is missing. The purpose of this paper is to explore the knowledge structure and frontier trends in respect of the IOA model applied to CF research using scientometric visualization analysis. The main findings of this paper are as follows. (1) Published articles show a two-stage increase in the period 2008 to 2021, and present a complex academic network of countries, authors, and institutions in this important domain. (2) The classic studies are mainly divided into three categories: literature reviews, database application introduction, and CF accounting in different scales. (3) The research hotspots and trends show that the research scales tend to be more microscopic and applications of models tend to be more detailed. In addition, supply-chain analysis and driver-factor analysis will probably become the main research directions in the future.

Currently, the integrated development of agriculture and tourism is one of the most critical strategic measures in China. The rapid growth of agricultural tourism integration presents the typical characteristics of expanding regional differences. Exploring the impact of agricultural tourism integration on the growth of green total factor productivity in agriculture has important theoretical and practical significance. This study constructs a comprehensive index system for agricultural tourism integration, measuring the development level of agricultural tourism integration in 30 sample provinces from 2008 to 2018. Using the generalized system method of moments approach and Tobit model, the impact of agricultural tourism integration on agriculture was empirically tested, and the regulatory role of rural human capital was discussed. It was found that agricultural tourism integration contributes significantly to the improvement in green total factor productivity in agriculture, with rural mobility human capital, education human capital, and health human capital all playing a significant positive moderating role in this process. Finally, it is recommended that priority be given to agricultural tourism integration in the policy framework, promoting industrial chain upgrading, raising investment in rural infrastructure, and upgrading rural human capital levels to contribute the rural economic development.

Focusing on the construction of the National Big Data Comprehensive Pilot Zone (NBDCPZ), we adopted the difference-in-differences model to examine the impact of big data on green total factor productivity (GTFP), using data from 276 cities in China for the period of 2012 to 2019. We also conducted mediating effects and moderating effects tests to explore whether a factor exists through which, or relying on it, big data influences the GTFP. The result of the baseline regression showed that the construction of the NBDCPZ significantly increased GTFP. After a series of robustness tests, this conclusion remains valid. In addition, we examined the mediating effects of industrial structure and green innovation and found that the construction of the NBDCPZ increased the level of GTFP, mainly by promoting industrial structure upgrading and green innovation activities. We identified the moderating effects of different types of environmental regulation on the relationship between urban digital transformation and GTFP and found that market incentive environmental regulation (MIER) has a significant positive moderating effect on big data increasing GTFP. Conversely, the moderating effects of command and control environmental regulation (CCER) and social participation environmental regulation (SPER) were found to be insignificant. These findings suggest that urban digital transformation, through its contribution to increasing GTFP, is an important pathway to high-quality development.

Purpose: Given the inevitable transition to renewable resource utilization and the urgent need to reduce carbon emissions, this study conducted quasi natural experiments to assess the impact of renewable resource utilization on carbon emissions based on the national “urban mining” demonstration bases (NUMDB).Design/methodology/approach: This study uses panel data from 275 prefecture-level cities in China from 2006 to 2019. The paper selects NUMDB as the proxy variable and conducts a quasi-natural experiment using a multi-period differences-in-differences model. We examine the impact of NUMDB on reducing carbon emissions, and then deeply explore its mechanism and spatial spillover effect.Findings: This study found that: (1) the construction of NUMDB can significantly decrease the carbon emission in the host cities; (2) NUMDB’s construction has more significantly reduced the carbon emission in regions with higher levels of circular economy development, green technology innovation, regional environmental pollution, digital economy development and financial development; (3) by means of green technology innovation, optimized energy structure, and high-quality talent aggregation, NUMDB reduces urban carbon emissions; (4) NUMDB construction positively affects the carbon reduction efficiency of neighboring regions.Research limitations/implications: We propose corresponding policy suggestions to further promote the carbon emission reduction effect of NUMDB and develop the renewable resources industry in China based on the research findings.Practical implications: The contributions of this paper are as follows. Our study contributes to expanding the research scope on the environmental impact of the renewable resource industry, as there are few quantitative studies in this area.Social implications: We further consider the spatial heterogeneity of policies and analyze the carbon reduction effect of the NUMDB from the city level, which is beneficial to exploring more targeted and operable carbon reduction paths.Originality/value: This study on identifying the causal relationship between renewable resource utilization and carbon emission reduction helps to explore the sustainable development path of renewable resource more comprehensively. Meanwhile, this paper provides a reference for other countries to improve the utilization of renewable resource and effectively reduce carbon emissions.