Search Results (5)

This paper proposes a policy-oriented framework to evaluate agricultural sustainability and productivity within agri-food supply chains (AFSCs), focusing on the Tunisian context. The Analytic Hierarchy Process (AHP) was applied to identify the most appropriate Total Factor Productivity (TFP) methods, based on criteria derived from expert interviews and literature review. Three approaches, the Solow Residual, Törnqvist–Theil, and Divisia indices, were assessed. The Divisia (AHP weight = 0.918) and Törnqvist–Theil (AHP weight = 0.547) indices achieved the highest rankings, reflecting their suitability for multi-output systems and robust productivity decomposition. Sustainability-related Key Performance Indicators (KPIs) were categorized as inputs and outputs and monetized using shadow prices sourced from academic, institutional, and field data. Applied to 2015–2020 data, overall TFP declined by 59.5%, mainly due to reduced resource-use efficiency, declining agricultural value added, and increasing input costs. Unlike prior studies focused on farm-level productivity, this framework integrates TFP with a multi-criteria sustainability assessment to enhance evidence-based policymaking. The results demonstrate that methodological choices substantially shape sustainability conclusions and highlight the added value of price-aware, multi-output indices for complex agricultural systems. Overall, the proposed AHP–TFP approach provides a transparent, adaptable, and policy-relevant tool for evaluating sustainable productivity in the agri-food sector. Full article

The Solow residual method, traditionally pivotal for calculating total factor productivity (TFP), is typically not applied to green TFP calculations due to its exclusion of undesired outputs. Diverging from traditional approaches and other frontier methodologies such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), this paper integrates undesired outputs and three types of spatial spillover effects into the conventional Solow framework, thereby creating a new spatiotemporal econometric Solow residual method (STE-SRM). Utilizing this novel method, the study computes the industrial green TFPs for 280 Chinese cities from 2003 to 2019, recalculates these TFPs using DEA-SBM and Bayesian SFA for the same cities and periods, and assesses the accuracy of the STE-SRM-derived TFPs through comparative analysis. Additionally, the paper explores the statistical properties of China’s urban industrial green TFPs as derived from the STE-SRM, employing Dagum’s Gini coefficient and spatial convergence analyses. The findings first indicate that by incorporating undesired outputs and spatial spillover into the Solow residual method, green TFPs are computable in alignment with the traditional Solow logic, although the allocation of per capita inputs and undesired outputs hinges on selecting the optimal empirical production function. Second, China’s urban industrial green TFPs, calculated using the STE-SRM with the spatial Durbin model with mixed effects as the optimal model, show that cities like Huangshan, Fangchenggang, and Sanya have notably higher TFPs, whereas Jincheng, Datong, and Taiyuan display lower TFPs. Third, comparisons of China’s urban industrial green TFP calculations reveal that those derived from the STE-SRM demonstrate broader but more concentrated results, while Bayesian SFA results are narrower and less concentrated, and DEA-SBM findings sit between these extremes. Fourth, the study highlights significant spatial heterogeneity in China’s urban industrial green TFPs across different regions—eastern, central, western, and northeast China—with evident sigma convergence across the urban landscape, though absolute beta convergence is significant only in a limited subset of cities and time periods. Full article

Calculating China’s industrial total factor productivity (TFP) at the prefectural level comprehensively and accurately is not only an inevitable requirement for China’s industrialization to enter the new development stage of “improving quality and efficiency”, but also a practical need for TFP improvement at the industrial level. Based on the improved Solow residual method with the general nesting spatial model embedded, this paper comprehensively calculated the industrial TFPs of 280 prefectural cities in China from 2003 to 2019, and undertook a detailed analysis of the spatiotemporal evolution law of the calculation results through Dagum’s Gini coefficient and kernel density estimation. Three main conclusions have been drawn in this paper. First, there is an apparent spatial difference among the industrial TFPs of the prefectural cities in China. It is the poorest and has an evident declining trend in northeast China, and best in eastern China, while the development of central and western China is between east and northeast China. Second, the spatial difference level of industrial TFPs of the prefectural cities in China shows a general development trend of firstly falling and then rising. Comparatively speaking, the contribution of intra-group differences is low, while the contribution of inter-group and the intensity of trans-variation are high. Third, the spatiotemporal evolution of China’s industrial TFPs at the prefectural level has the following characteristics: the overall distribution curve moves firstly towards the right and then left, the kernel density at the peak point continuously declines, the distribution ranges are firstly widening and then narrowing, and the tails of the distribution curve are constantly extending. Meanwhile, the distribution figures of the kernel density estimation in different regions show apparent heterogeneity. Full article

Achieving sustainable development has become the consensus of the development of human society, but many of the cities in underdeveloped resource-rich areas (UDRRAs) are sacrificing natural resources and the environment for local economic growth, which hinders the regional sustainable development. This paper uses the Solow residual method to calculate the total factor resource efficiency (TFRE) of 114 resource-based cities to assess the extent to which these cities trade resources and environment for development and identifies 59 cities in UDRRAs. The results of the study are as follows: a. Cities in UDRRAs are mainly distributed in the central and western regions and in ecologically fragile areas. b. The contribution rate of the TFRE to the economic growth of cities in UDRRAs is only 19.30%, while the contribution rate of the factor input is as high as 80.70%, and there is a phenomenon of the “resource curse” at the urban level. c. The carbon dioxide input contributed the most to the economic growth of cities in UDRRAs, accounting for 52.26%. d. The problems faced by the different types of cities in UDRRAs are quite different, especially the declining cities in UDRRAs urgently need to formulate sustainable development paths. Finally, we put forward some reference opinions on the sustainable development path of cities in UDRRAs. Full article

This paper focuses on the total factor productivity (TFP) and high-quality economic development in China by examining 11 Chinese provinces and cities in the Yangtze River Economic Belt from 2007 to 2018. We use the Solow residual method to calculate the TFP growth rate of the 11 provinces and cities. Based on the panel data, we have analyzed the influencing factors of TFP theoretically and empirically from the overall region and upstream region, and midstream region and downstream region, respectively. The regression results show that: (1) The whole characteristics generally show the TFP growth trend of the upstream region, midstream region and downstream region are consistent with that of the overall region, and the growth rate of TFP slows down gradually. Meanwhile the differences in TFP growth between the upstream region, midstream region and downstream region show an increase at first and then a decrease. (2) Regarding the influencing factors, there are differences in the direction and extent of the impact of each factor such as the level of openness, R&D investment, industrial structure, government expenditure and human capital on the TFP of the overall region, upstream region, midstream region and downstream region. (3) Based on the results of the theoretical and empirical analysis, we have proposed a series of measures for the sustainable high-quality development of the Yangtze River Economic Belt. Full article