Table of Content

    20 February 2023, Volume 25 Issue 1


    ZHU Zhiming, GUI Mengting, LI Hongyan
    2023, 25(1):  1-13.  DOI: 10.13776/j.cnki.resourcesindustries.20220715.001
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    This paper estimates Jiangsu's carbon emission and absorption and forecasts its future carbon balance potential, offering reference for China to reach strategic objectives of 2030 carbon peak, and 2060 carbon neutralization (dual carbon objectives), and to materialize ecological civilization construction. This paper establishes an indictor system to forecast Jiangsu's carbon balance potential and improves STIRPAT model based on 1996 to 2019 carbon emission data and LMDI factors, which is used to forecast Jiangsu’s 2020 to 2060 carbon emission under 9 scenarios. GM(1, 1) is employed to forecast Jiangsu’s future carbon absorption and 2004 to 2060 carbon balance evolution. Results show that Jiangsu’s carbon emission grows fast, most contributed by population, income and energy structure, but offset by energy intensity and carbon emission intensity. According to the forecasted results of STIRPAT model, nine scenarios can be classified as high carbonhigh growth, intermediate carbonintermediate growth and low carbonlow growth in terms of cabon peak and time. The premium scenario 3 in the lowcarbonlow growth can reach carbon peak as early as in 2029, at 330 0386 kt carbon dioxide, and decline to 242 7419 kt in 2060. Jiangsu’s carbon absorption is forecasted to growth slowly, up to 30 mt carbon dioxide in 2053, and 30955 84 mt in 2060. Jiangsu will face a lagging carbon balance, with a peak deficit up to 302860 3 mt carbon dioxide in 2029, falling to 211786 0 mt in 2060. Carbon balance pressure index will fall to 784 in 2060 from 1216 in 2025, suggesting a big pressure over Jiangsu. Jiangsu has a limited ecological carbon absorption capacity, facing a big challenge on carbon balance even according to the premium scenario 3. This paper presents suggestions on adjusting energy high carbon structure, boosting green technical innovation, decreasing carbon emission, increasing green land area, protecting natural environment, raising carbon absorption capacity, developing green carbon reduction and technical carbonfixing, diminishing carbon deficit and mitigating cabon balance pressure from carbon emission, carbon absorption and carbon balance, which helps China reach 2060 carbon neutralization.

    TIAN Ze, XIAO Lingying, LIANG Wei, et al
    2023, 25(1):  14-26.  DOI: 10.13776/j.cnki.resourcesindustries.20220630.001
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    Yellow River stream is entering a period of ecological protection and quality development. Study on the coupling coordination between its industrial green low-carbon transformation and quality economic development is of significance in reaching the goals. This paper applies temporal-spatial range entropy weights to estimate Yellow River stream’s 2009 to 2019 industrial green low-carbon transformation and quality economic development levels, and uses calibrated coupling coordination model to measure their coordination, and employs Dagum Gini coefficient and decomposition to study the regional variance between industrial green low-carbon transformation and quality economic development with Kernel density estimation dispatched to study its dynamics. Its industrial green low-carbon transformation level is at 0.449, and quality economic development level at 0.490, with annual growth rate at 5.84% and 7.1%, respectively, overall in a upgrading trend, but industrial green low-carbon transformation is notably behind its quality economic development. Their coordination level is low at 0.55, with annual growth rate at 3.2%, 0.629 in 2019, a turning point from off coordinated to preliminarily coordinated. The both are not at the same pace in coordination with coupling coordination showing a spatial distribution of “high-in-lower stream, middle-in-middle stream and low-in-upper stream”, 0.59, 0.561 and 0.529, respectively. Their overall spatial variance is diminishing, primarily contributed by their regional variance with a contributing value at 46.97%, secondarily by the internal variance with a contributing value at 33.54%. This paper presents suggestions on constructing regional green low-carbon industrial system, exploring differentiated policies and boosting trans-stream cooperation.
    ZHAO Zhendong, LIU Guoqing, XIN Jianghui, et al
    2023, 25(1):  27-39.  DOI: 10.13776/j.cnki.resourcesindustries.20220802.001
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    HFCV industry is a sustainable direction to fuel transformation and automobile industry; it catches China’s much attention. Due to the lagging fuel electric automobile industry, its operation is still in demonstration. Industrial evolution and changes are directly related to its development strategies and policies on HFCV industry; industrial predictability is a vital basis for a successful industrial policy. This paper, aiming at boosting China’s industrialization process of HFCV and at fulfilling the industrial policies, establishes a systematic dynamics causality model and stock flow model of HFCV in Jiangsu province based on related policies, reference and industrial plannings, which have been validated. Its evolutional trend and scenarios simulation and policy also reveal the impacts of different financial allowance policies on this sector. Under the current policies, it is hard to reach the 2030 objectives on HFCV stock and hydrogen fuel stations in that the purchase allowance policies have little impact on sales. Decreasing hydrogen price and increasing hydrogen fuel stations can boost this sector’s scale and industrialization. Gas-hydrogen stations, lower in cost, can have a better operating performance in meeting the demands of hydrogen compared with hydrogen stations. This paper presents suggestions on increasing inputs on HFCV industry, adjusting allowance policies, optimizing financial allowance allocation, focusing on hydrogen energy infrastructure construction, especially the hydrogen stations, decreasing purchase allowance. This also gives proposals on demonstrating purchase inputs at the prior stage, intensifying hydrogen(gas-hydrogen) station construction at the later stage. This study provides helpful references for governments to make appropriate industrial policies on HFCV industry.



    LIU Rong, WU Qiuhao
    2023, 25(1):  40-50.  DOI: 10.13776/j.cnki.resourcesindustries.20221018.001
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    This paper selects 8 pieces of 2016 to 2020 policy samples to establish a PMC model, which is employed to construct an evaluation system of water-saving policies on 10 tier 1 variables and 45 tier 2 variables. Text extraction is used to select the key words in the policies, and inputs & outputs from water-saving evaluation system are given values. PMC indicators and curves are employed to discover the pros and cons of policies. Representative water-saving policies in “the 13th  five-year plan” are evaluated to summarize water-saving experience, execution progress and issues aiming at providing references for further water-saving management, and at facing new water resource situation in China. 8 pieces of policies have an average PMC indicator at 7.3, generally at the level of excellence. Five indicators in single policy which are above 7 are at the level of excellence, and at the level of acceptance if three above 6, suggesting an overall appropriate water-saving policies design in China, but along with issues in insufficient policy effectiveness, lacks of stimulating approaches, limited water saving, single viewing angle, imbalanced tool usage, and inadequate regional features. This paper presents suggestions on planning China’s water-saving policies under multiple perspectives to increase policy quality, on increasing encouraging and constraining approaches, including governmental allowance, capital investment, technical supports, preferential taxation, welcoming more participants in saving water, adding on-demand policy tools, pushing water-saving industrial development and intensifying policy’s regional features. This summary presents reference for further water-saving work.
    ZHAO Min, CHU Peipei
    2023, 25(1):  51-66.  DOI: 10.13776/j.cnki.resourcesindustries.20220915.001
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    Rural industrial integration is a fundamental approach to agricultural industrial bloom, and also a key solution to rural, agricultural and farmers’ issues. Increasing rural industrial integration level is a critical means to agricultural economy. This paper uses entropy weights to estimate 31 Chinese provinces’ 2008 to 2020 rural industrial integration level, and analyzes the spatial convergence and differentiated characteristic based on spatial conditionalβconvergence model. Results show that rural industrial integration level is low, only 40% of the target. Regionally, horizontal rising rate displays a deceasing tend from east, west and to central. Provincewide variance is less than cross-provincial. China’s rural industrial integration level is outstandingly of spatially conditionalβconvergence, with its convergence period shortened by 6 years. Rural industrial integration level is mostly contributed by financing agriculture, agricultural technical advances and farmers’ rising consumption level, but its convergence is slightly constrained by human capital. Spatially, rural industrial integration level in three areas is of notable “club convergence”, higher that the national average, also with a stronger spatial positive overflowing effect. Temporally, convergence rate in 2016 to 2020 is higher than 2008 to 2015, in which economic development and human capital work variably in direction and extent. This paper presents suggestions on optimizing top design, and modernizing agricultural industries, boosting financing agriculture, agricultural technical advances and farmers’ consumption level, improving imbalanced agricultural industrial integration, paying attention on talents, fulfilling the positive promotion of human capital on rural industrial integration level and high value convergence, and establishing a concept of wholly national rural industrial integration system and realizing a balanced development of nationwide rural industrial integration.
    LIU Wanbo, ZHANG Hui, WANG Hui, et al
    2023, 25(1):  67-78.  DOI: 10.13776/j.cnki.resourcesindustries.20220525.001
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    This paper, based on recognition of urban multiple-centralization spatial structure, analyzes their spatial conglomeration of different functionalities, and studies the expanding trend of suburbanization of downtown’s manufacturing in Shenyang, and presents references for upgrading Shenyang’s downtown manufacturing and urban spatial organization. This paper uses POI data, ArcGIS10.7, GeoDa1.12 and Crimestat 3.3 softwares to identify 2020 urban multiplecentralized spatial structure by means of core density, spatial hotspot conglomeration, standard variance eclipse and spatial auto-correlation, and measures their spatial correlation among different functional facilities, and analyzes the manufacturing conglomeration in 1998, 2008 and 2020. In 2020, Shenyang’s urban spatial structure is characterized by multiple-centralized, layer-radiated, forming three-hierarchy urban centralized systems through a urban planning one-major-four-minor, generally consistent with Shenyang’s 2011 to 2020 overall urban planning. Besides single center in downtown, each minor city also has itself minor center, corresponding to multiple-centralized urban spatial structure. During 1998 to 2020, manufacturing has been migrating outward from downtown, mainly distributing in the third and forth rings in west and south, like Tiexi new city and Yongan new city. Shenyang’s functional space displays southwest to northeast extension with center-suburb urban function system. Spatial correlation exists among different functional districts. Industrial district represented by manufacturing is exclusive with single urban function, having strong spatial correlation among residential, commercial, public administrative and functional facilities as a comprehensive servicing center. This paper presents suggestions for Shenyang on further improving urban functions and optimizing industrial layout, and boosting living services in the industrial district which is of urban core production functions.

    NIE Zhiping, WANG Yiwen
    2023, 25(1):  79-96.  DOI: 10.13776/j.cnki.resourcesindustries.20220816.001
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    Study of either retroaction effect or crowding out effect between environmental information disclosure and companies’ innovation is key to reaching a win-win between environmental protection and economic development. This paper uses legality, reputation, signal transmission and stakeholder theories to study the impacts of environmental information disclosure on resource companies’ innovation. Results show environmental information disclosure can notably increases resource companies’ innovative level, hard disclosure works more compared with soft disclosure. Negative media supervision will offset companies’ reputation and financing advantage brought from environmental information disclosure to some extent, then constraining resource companies’ innovative capacity, but other media will not promote their innovative levels upon environmental information disclosure. Governmental supervision and analysts’ attention will not positively adjust the relation between environmental information disclosure and innovative level of resource companies, but analyst’s attention will work if resource companies discloses hard environmental information, which is a good practice for green innovative transformation. Governments need to further optimize environmental information disclosure system in a unified standard, and to fulfill media supervision and analysts’ social supervision.
    CHENG Ming, YAN Feng, DU Tingxia, et al
    2023, 25(1):  97-108.  DOI: 10.13776/j.cnki.resourcesindustries.20220829.001
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    As economic globalization develops, R&D plays a key role in increasing innovative capacities and booting economic structural optimization. Study on dynamic relation among R&D inputs, innovative performance and economic growth can give us a deep understanding on regional innovative transformation, and provide references on innovative drives changes. This paper uses Shandong’s 2012 to 2020 16 prefectures’ panel data to establish PVAR model, and applies pulse response function and deviation to their interaction among R&D inputs, innovative performance and economic growth, and their variance in Shandong’s three economic zones. R&D inputs show one stage lagging behind economic growth, but faster in capital and Jiandong economic zones with their contributing rates up to 40.1% and 39.8%, respectively. Economic growth plays an adverse role against innovative performance, more in Jiaodong economic zone. Innovative performance promotes economic growth, with a contributing rate up to 85.9% in southern Shandong economic zone. R&D inputs have a low contributing rate to innovative performance, lagging 1 to stages in Jiaodong economic zone. Innovative performance promotes R&D inputs in a lagging 1 stage, but faster in capital and Jiaodong economic zones, with a contributing rate up to 80.4% in southern Shandong economic zone. The results generally show an interactive promotion among economic growth, R&D inputs and innovative performance. In capital economic zone, economic growth and R&D inputs have promoting roles on innovative performance, with a low conversion capacity from economic growth and R&D inputs to innovative performance. In Jiaodong economic zone, an outstanding promoting role exists both between economic growth and R&D inputs, and between R&D inputs and innovative performance, but low in the conversion rate between innovative outputs and economic growth. In southern Shandong economic zone, economic growth and R&D inputs have promotion on innovative performance, but insufficient beneficial interaction among the three.

    XUE Song, ZHAO Jingjing, YANG Tao
    2023, 25(1):  109-121.  DOI: 10.13776/j.cnki.resourcesindustries.20220526.001
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    Knowledge sharing and green innovation are two key factors in shaping industrial sustainable competitiveness, and play a vital role in infrastructures’ transformation to sustainability. This paper, aiming at their collaboration among knowledge sharing, green innovation and infrastructures sustainable development, uses the relation structure in the measurement table to establish an interactively simulated model, which is verified and corrected by confirmed data from questionnaire. Knowledge sharing channel, behavior and environment promote the sustainable development of infrastructures, most contributed by knowledge sharing behavior. Green technical innovation and green system innovation play a positive role on the sustainable development of infrastructures, of which green technical innovation plays the bigger part. Three approaches, green technical innovation to knowledge sharing channel, green system innovation to knowledge sharing environment, green technical innovation/green system innovation to knowledge sharing behavior, play a mediating role in promoting the sustainable development of infrastructures. Green innovation may further boost the indirect impact of knowledge sharing on the sustainable development of infrastructures, but less from its direct impact. This paper presents suggestions on encouraging knowledge sharing behavior, setting up knowledge sharing system, constructing knowledge sharing environment for a sustainable development. Green collaborative innovation promotes the infrastructures. This paper provides theoretical references for a quality sustainable development of infrastructures.
    NIE Zhiping, FAN Xiaowen
    2023, 25(1):  122-131.  DOI: 10.13776/j.cnki.resourcesindustries.20220614.001
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    Advanced manufacturing plays a key part in China’s quality economic growth, mainly driven by innovation, which needs pay attention on how it works. This paper uses 2013 to 2019 A stock-listed advanced manufacturing companies data to study the tie between their strategic variance and innovation investments. Their research & development (R&D) investments vary largely with the strategies; the bigger the strategic variance, the lower the R&D investment. Trade credit financing is one of the mediating paths to impacting their R&D investments, suggesting strategic variance will decrease their R&D investments through constraining the advanced manufacturing companies’ trade credit financing size, outstandingly on those with more constraints. Results are still robust after verified by tool variable and adjusted strategic variances. This conclusion helps China’s advanced manufacturing select appropriate strategic position and advance technical innovation, and verifies the importance of trade credit financing on advanced manufacturing’s technical innovations. Advanced manufacturing companies need to consider the possible economic aftermath if they are off the conventional strategies, largely impacting their R&D investments. Most advanced manufacturing companies are suggested selecting a following strategy if they are not strong in innovative resources; their pursuit of eccentric strategy may be harmful to their technical innovation. This paper gives suggestions for advanced manufacturing companies on treasuring their credits, intensifying the ties with customers and suppliers, which may relieve their financing constraints due to strategic variance, favorable for their technical innovations.

    HUANG Yushan, ZOU Chen
    2023, 25(1):  132-144.  DOI: 10.13776/j.cnki.resourcesindustries.20220615.001
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    As China’s strategy of world scientific & technical power develops, scientific & technical talents need more attention as part of regional competitiveness. This paper studies their inner, environmental and productive competitiveness of six regional integrations on scientific & technical talents in economy, industries, markets, ecological environment, infrastructure and public servicing in Yangtze River Delta, and uses 2010 to 2018 panel data of 41 cities to explore the heterogeneity of regional integration on talents competitiveness at different economic developing stages. Yangtze River Delta’s integration is currently in the form of market integration; innovation- and efficiency-driven urban integration are market integration and economic integration, respectively. Regional integration can attract more scientific & technical talents, boost their migration between industries and regions, increasing their internal competitiveness from quantity and quality. Regional integration provides a convenient and comfortable living environment, increases talents’ income, raises environmental competitiveness for talents. Regional integration also helps economic & scientific factors flow with removal of localized-benefit economy, and improves productivity of patent and economy, hence increases productive competitiveness of talents. Compared with innovation-driven cities, efficiency-driven cities pay more attention to increasing talents’ living level for promoting their quantity, but below innovation-driven cities both from patent and economic productive capacities. This paper presents suggestions on intensifying talents’ competitiveness in Yangtze River Delta from ecological green integration, public services integration and urban coordinated development in different stages.