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Improving green total factor productivity (GTFP) is an important theme. Whether collaborative agglomeration between logistics industry and manufacturing (LMCA) can effectively promote GTFP is worth further research. Based on the panel data of 284 cities in China from 2005 to 2018, GTFP is calculated by using the Biennial Malmquist-Luenberger produc...
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Context 1
... collaborative agglomeration is a higher stage of industrial agglomeration, which not only enlarges the knowledge spillover effect and crowding effect of a single industrial agglomeration but also produces economic, technological and knowledge linkages due to vertical or vertical linkages among heterogeneous industries. The three kinds of correlation effects produce different positive and negative effects to influence local city GTFP (as shown in the right half of Figure 1). ...
Context 2
... affects GTFP of surrounding cities through spatial spillover effects such as diffusion effects and siphon effects (as shown in the left half of Figure 1). In the process of continuous collaborative agglomeration of logistics industry and manufacturing, logistics and manufacturing facilities and other related infrastructure will be continuously improved. ...
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Citations
... According to Table 1, we can see that when defining productivity, the vast majority of scholars assess carbon productivity [7,8] or green productivity [5,16] from a sector-wide perspective or focus on the energy efficiency [18,19] of a particular sector. It is important to point out that the productivity measured from a sector-wide perspective is not precise to a particular sector, and it has been pointed out that industrial agglomeration occurs mainly in urban areas, making it difficult for the exchange and cooperation of industrial enterprises within cities to influence carbon emissions from agriculture, forestry, animal husbandry, and fishing [20]. ...
... The environmental and socioeconomic effects of agricultural mechanization have received a great deal of attention. Some scholars have also proposed that the advantages of mechanization options are increases in resource use efficiency, as well as labor and land productivity [14][15][16]. This is especially the case in countries with more efforts to improve the mechanization of smallholder agriculture. ...
Agricultural machinery is effective in improving food production and labor productivity, but it also raises environmental concerns. The impacts of agricultural machinery on the green total factor productivity (GTFP) of grain in China are still under debate in the scientific literature. This study proposed an integrated framework for confronting this issue. The findings suggest that both agricultural mechanization and the GTFP of grain demonstrate a consistent upward trend with moderate fluctuations between 2001 and 2019. By expanding the spatial pattern, there is a positive spatial correlation between them. In addition, we compared the results in three grain functional areas by using the spatial Durbin model (SDM). There were significantly positive spatial spillover effects in major grain-producing areas, which were attributed to the trans-regional operation of agricultural machinery and its carbon reduction effects on neighboring provinces. Notably, the direct effects in major grain-marketing areas and producing-marketing balance areas were significantly positive because agricultural machinery has played a critical role in filling the gap in local labor shortages in grain production. Accordingly, adaptive strategies including building the “Internet + agricultural machinery operation” platform, implementing the land consolidation suitable for machinery, and developing low-carbon agricultural machinery should be fully considered by Chinese policy-makers to promote mechanized agriculture and a low-carbon economy. The findings of this study can help us better understand the role of agricultural machinery in improving green grain productivity in China and thus have significance for the modern and green transformation of agricultural production systems.
... In particular, Zheng and He (2022) takes the Chengdu-Chongqing area as an example and pointed out that IC has an obvious promoting effect on local economic growth, although it may pose a negative spillover effect on the surrounding area. Similarly, Li et al. (2021a) reported that IC can promote the development of local and surrounding areas through the knowledge spillover effect, scale economy effect, and resource allocation effect. The study of Wu et al. (2022) also provided support for the promoting effect of IC on local and surrounding urbanization development. ...
The double-wheel drive of manufacturing industries and producer services industries is one of the key pathways to promote high-quality development relying on a modern industrial system. This paper explores the impacts of industrial co-agglomeration on regional economic growth in China with systematic consideration of static, dynamic, and spatio-temporal perspectives based on panel data for 285 prefecture-level cities from 2003 to 2020, employing the consolidated night-time light data. The empirical results show that industrial co-agglomeration significantly accelerates regional economic growth, especially high-tech intensity producer services industries, information industries, and finance industries. In addition, its spatial spillover effects are evidently established, which are characterized by cyclic accumulative, feedback features, and distance attenuation. Carrying out robustness tests, the preliminary regression results are verified. The heterogeneous influences are established across cities with different geographical locations, innovation capacities, and resource endowments. The further mechanism analysis indicates that industrial upgrading, technological progress, and efficiency enhancements account for the main channels for a sharp promotion in regional economic growth over the sample period. Furthermore, the government moderates this process more significantly than market forces do, especially when it comes to macroexogenous shocks that are regulated by the government. The findings of this study recommend policymakers to give full play to the positive externalities of industrial co-agglomeration and accelerate the industrial co-agglomeration process in a reasonable manner, so as to promote high-quality economic growth in China.
... The research results on the coupled and coordinated development of manufacturing and logistics industry mainly include the following three aspects. (1) The research objects mostly focus on the nationwide coupled and coordinated development of the two industries [2], and some scholars select typical regions and provinces as research objects, such as the Yangtze River Economic Belt [3], East China [4], Guangxi Province [5] and Heilongjiang Province [6], which play a guiding role in the coupled and coordinated development of the two industries. (2) Most of the studies are on linkage efficiency measurement and coupling coordination analysis. ...
The coupled and coordinated development of the manufacturing and logistics industries has become an inevitable choice for achieving high-quality development in both sectors. In this study, we focused on nine provinces located in the Yellow River Basin and analyzed panel data from 2010 to 2021. Our analysis, based on the super-efficient SBM-undesirable model, revealed that the coupling and coordination efficiency between the two industries in the region is moderate, with significant regional disparities. Additionally, using the Global Moran’s I and the local Moran’s I, we tested the spatial autocorrelation of the two industries and analyzed their spatial interaction effect using SDM. The study reveals that the manufacturing and logistics industries in the Yellow River Basin exhibit moderate coupling and coordination efficiency with significant regional variations. We found that the logistics industry plays a more supportive role in the manufacturing industry, particularly in Henan and Shandong provinces. Spatial spillover effects in terms of informationization, openness to the outside world, and energy consumption are more significant, while infrastructure investment does not exhibit significant spatial interaction effects. Based on our findings, we propose relevant development strategies for the two industries.
... Unfortunately, the literature explaining the mechanism of industrial agglomeration on GTFP from the urban regional level is still scarce, and the results and directions are inconclusive. The main findings of the studies are the promotion (Mitra and Sato, 2010;Lin and Tan, 2019;He et al., 2022), inhibition (Leeuw et al., 2001;Zhang and Wang, 2014), nonlinear (Chen and Tang, 2018;Zhang, 2014;Ji and Li, 2020;Li et al., 2022;Zhu and Liu, 2021), and spatial effects (Lu et al., 2021;Li et al., 2021;Zhang et al., 2021) of industrial agglomeration on urban GTFP. In addition, the high dependence of innovation on proximity makes innovation activities most active and abundant at the urban level, and the market-oriented flow of innovation elements and the integration of spatial proximity have led to the formation and development of innovation agglomeration (Wang et al., 2021a). ...
Under the background of China's high-quality economic growth with total factor productivity (abbreviated as TFP) growth as a key driver, this study attempts to integrate heterogeneous industrial agglomeration, innovation agglomeration, and institutional distance into the theoretical analysis framework of urban green total factor productivity (abbreviated as GTFP). The impact and effect of industrial agglomeration on urban GTFP growth, and the moderating role of innovation agglomeration and institutional distance in the above effects are examined empirically. The findings indicate a significant inverted ''U'' relationship between the three types of industrial agglomeration and urban GTFP growth. From the critical values, the three types of industrial agglomeration do not cross the critical value on the whole, showing the promoting effect on the growth of urban GTFP. Under the different types of city divisions (small, medium and large cities, east-central-west cities, and north-south cities), the effect of the three types of industrial agglomeration on GTFP growth in different types of cities has typical heterogeneity. Innovation agglomeration and institutional distance play a positive regulatory role in the impact of industrial agglomeration on the growth of urban GTFP. It is further found that innovation agglomeration plays a threshold role in the impact of industrial agglomeration on urban GTFP, i.e., the positive contribution effect of three types of industrial agglomeration on urban GTFP growth gradually increases with the increase of urban innovation agglomeration. This paper introduces the research results of industrial agglomeration on urban GTFP and provides policy implications for improving GTFP.
... Hence, China has formulated a series of emission-reduction policies to control carbon emissions to alleviate the environmental pressure [4][5][6]. Green Total Factor Productivity (GTFP) is a total factor measurement system that removes non-desired outputs and represents the level of SD of regional economies [7,8]. In this context, China's regional economic level will more or less change with the adjustment of carbon emission reduction policies. ...
With the rapid growth of China’s economic growth, a large number of greenhouse gas emissions have led to a significant increase in environmental pressure. Currently, China has not yet achieved a good balance between greenhouse gas emissions and economic growth. To improve the sustainable development of China’s regional economy and effectively control domestic CO2 emissions, research is conducted to analyze the trend of regional economic change based on carbon emission policies. This study looks for suitable paths to achieve sustainable development of the regional economy. In this study, CO2 emissions were incorporated into an economic model to calculate the Green Total Factor Productivity (GTFP) efficiency value and its growth rate in each region of China. This was done to examine the productivity of each region in China. and it also aims to discuss the driving factors behind it, so as to give relevant policy suggestions that can help China’s sustainable economic development. The ultimate goal is to achieve sustainable RE development. The method used to measure the GTFP efficiency was the slacks-based measure (SBM) based on the data envelopment analysis (DEA) technique. The regression analysis of the relevant drivers was based on the regression analysis of the panel data model. The research results show that the level of urbanization and industrial structure were the main influencing factors for the increase of CO2 emissions. Consequently, macro-regulation can appropriately reduce CO2 emissions. In addition, the implementation of carbon emission reduction policies such as industrial structure optimization, education investment, and market-oriented reform also promote the sustainable development of the regional economy. Therefore, appropriate carbon emission reduction policies can improve the level of sustainable development of the regional economy. It also can ensure the stability of the regional environmental level.
... On economic agglomeration and carbon productivity in Chinese… overall economic growth. Over the last two decades, more and more research attention has focused on economic agglomeration and its effect on the natural environment (Hy et al., 2020;Li et al., 2021a;Xie et al., 2021). Economic agglomeration has an obvious effect on production scale, raising the levels of energy consumption with rising carbon emission as a result of intensive use of various kinds of production factors, especially fossil fuels (Liu et al., 2017a(Liu et al., , 2017bRen, 2003;Sun & Yuan, 2015). ...
Economic agglomeration has played a key function in China’s economic transformation. Meanwhile, structural adjustment and external pressure to reduce carbon emission necessitate a deeper investigation into its relationship with carbon productivity. This paper measures economic agglomeration and carbon productivity of 281 cities in China in 2003–2017. With the purpose of exploring the spatial effect of economic agglomeration on carbon productivity, a spatial Durbin model is constructed. In addition, the mediation effect model is also adopted for examining the transmission mechanism of economic scale and technological progress on economic agglomeration as well as carbon productivity. A heterogeneity test is also carried out to uncover the varying effects of economic agglomeration on carbon productivity. The empirical results suggest that economic agglomeration raises carbon productivity not only in the local cities but also in the neighbouring ones. The impact of economic agglomeration on carbon productivity becomes more potent as the level of economic agglomeration increases. For cities with low level of economic agglomeration, the effect is mainly achieved through the expansion of economic scale, but for cities with high level of economic agglomeration, the effect is mainly achieved through technological progress. Therefore, we suggest encouraging carbon productivity improvement through technology effect in cities with high economic agglomeration level, while enhancing carbon productivity through scale effect in cities with low economic agglomeration level.
... The research direction is gradually shifted from the micro to the macro level. It tends to the provincial [7,10], municipal [11,12], and other levels around the spatiotemporal evolution characteristics and impact factors of GTFP. ...
This paper considers GTFP of energy consumption and environmental pollution from a sustainable perspective as a measure of the evolutionary efficiency of manufacturing industries. It uses the super-efficiency SBM model to calculate the GTFP efficiency values of manufacturing industries in 30 Chinese provinces from 2011 to 2019. Moran’s index and the GTWR model were used to study the spatial correlation and impact factors of GTFP. The results found that the following. (1) The overall level of GTFP in China’s manufacturing industry from 2011 to 2019 rose progressively, and the level of GTFP between different regions gradually reduced. (2) The spatial correlation of GTFP in China’s manufacturing industry is significantly positive, with a positive spatial spillover effect. (3) The level of manufacturing GTFP is affected by economic development, investment, and other factors. (4) There is spatiotemporal heterogeneity in the impact factors of manufacturing GTFP. According to empirical research focusing on the goal of sustainable development, it is proposed to increase the use of clean energy and reduce environmental pollution. To carry out green development according to local conditions, the eastern region will strengthen the development of new energy manufacturing and continue to increase investment in innovation, and the central and western regions will strengthen environmental supervision, accelerate industrial transformation, and attract more foreign investment.
... Lanaspa et al. [27] argue that synergistic effects and co-agglomeration economies between manufacturers and intermediate producer services generate geographical clusters that ultimately contribute to regional economic growth. Li et al. [28] find that co-agglomeration of logistics and manufacturing industries can promote green total factor productivity in local and surrounding areas through economies of scale, the knowledge spillover effect, and the resource allocation effect. (2) Research on the mechanism of the role of industrial co-agglomeration on the environment. ...
In the era of the digital economy, the information and communication technology (ICT) industry has opened up a new round of expansion, while forming co-located development in the space. ICT industrial co-agglomeration has tremendous advantages in promoting economic development and achieving carbon neutrality goals. This paper analyzes the spatio-temporal characteristics of ICT industrial co-agglomeration and carbon productivity from 2009 to 2019 in China. It empirically explores the impact of ICT industrial co-agglomeration on carbon productivity using a systematic GMM model. Additionally, it analyses the spatial and temporal heterogeneity of ICT industrial co-agglomeration and other factors affecting carbon productivity using a geographically and temporally weighted regression (GTWR) model. The findings are as follows: (1) China’s ICT industrial co-agglomeration and carbon productivity show an upward trend. Additionally, their characteristic of regional distribution is east–high and west–low. (2) ICT industrial co-agglomeration has a positive association with carbon productivity. (3) The impact of ICT industrial co-agglomeration on carbon productivity has significant spatial and temporal heterogeneity. The regression coefficient of ICT industrial co-agglomeration increases continuously during the study period, and the degree of impact is relatively larger in Northern China. As the degree of ICT industrial co-agglomeration continues to increase, its positive impact on carbon productivity across China is deepening. The findings of this paper complete the research on the impact of ICT industrial co-agglomeration on carbon productivity, and the related policy recommendations provide useful references for the digital economy and sustainable development.
... Although the existing total factor productivity evaluation system provides a large number of objective evaluation schemes; for example, it puts forward effective schemes from the heterogeneous level [4] and the spatial panel level [5]. However, the analysis of the factors affecting total factor productivity is relatively simple, and for total factor productivity, it often only focuses on one aspect of total factor productivity, such as green total factor productivity [6] or enterprise total factor productivity [7], and there is little research on China's total factor productivity. Most total factor productivity studies are based on the selected targeted evaluation model to achieve the optimal analysis effect [8]. ...
My country’s current research on the influencing factors of total factor productivity has problems such as single evaluation method, low efficiency, and poor overall level in terms of evaluation methods and evaluation efficiency. Based on this, this study divides the financial structure into three traditional sections, banking, securities, and insurance, and uses the DEA model to study the temporal and spatial differences of the financial structure’s influence on the total factor productivity of the four major political and economic regions of China’s eastern, western, central, and northeastern China. First, establish a DEA model based on data mining algorithms, combine financial data comparisons over the years, to achieve a quantitative analysis of the financial structure’s impact on China’s total factor productivity, calculate financial efficiency, and then combine the DEA analysis data model with the grey correlation method. Analyze its internal influence rules, and design experiments for model verification analysis. The results show that the DEA analysis model can realize 8 iterations of data on the impact of financial structure on China’s total factor productivity, and its evaluation accuracy can reach more than 96.2%.
