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A Systematic Literature Review of Green and Sustainable Logistics: Bibliometric Analysis, Research Trend and Knowledge Taxonomy

1 College of Defense Engineering, Army Engineering University of PLA, Nanjing 210042, China; moc.361@1080iurner (R.R.); moc.361.piv@lz-nehC (Z.C.)

2 College of Civil Engineering, Nanjing Tech University, Nanjing 211816, China; moc.361@9708obnus

Jianjun Dong

3 Research Institute for National Defense Engineering of Academy of Military Science PLA China, Beijing 100850, China; moc.liamxof@xdgl-cyc

Zhilong Chen

Ever-growing globalization and industrialization put forward impending requirements for green and sustainable logistics (G&SL). Over the past decades, G&SL initiatives triggered worldwide deliberations, aiming at easing negative transport externalities and improving supply chain performance. This review-based paper attempts to offer a joint quantitative and qualitative understanding for the overall evolutionary trend, knowledge structure, and literature gaps of the G&SL research field. Employing the science mapping approach, a total of 306 major paper published from 1999 to 2019 were retrieved, elaborated on, and synthesized. Visualized statistics regarding publication years, journal allocation/co-citation, inter-country/institution collaboration, influential articles, co-occurred keywords, and time view clusters of research themes were analyzed bibliographically. On this basis, a total of 50 sub-branches of G&SL knowledge were classified and thematically discussed based on five alignments, namely (i) social-environmental-economic research, (ii) planning, policy and management, (iii) application and practice, (iv) technology, and (v) operations research. Finally, the current knowledge obstacles and the future research opportunities were suggested. The findings contribute to portray a systematic intellectual prospect for the state quo, hotspots, and academic frontiers of G&SL research. Moreover, it provides researchers and practitioners with heuristic thoughts to govern transportation ecology and logistics service quality.

1. Introduction

Sustainable development has inspired many green and sustainable logistics (G&SL) activities to reduce the negative effects of freight transportation [ 1 ] and improve positive environmental and social feedbacks. From long-haul heavy-duty logistics to intra-city distribution, road-based freight transportation systems generate tremendous negative externalities in daily operations [ 2 ], including pollutant emissions, congestion, traffic accidents, noise, visual interference, infrastructure failure and resource waste [ 3 ]. Moreover, these negative externalities, together with the disadvantages of logistics system itself (e.g., limited intelligentization, personnel dependence and vulnerability [ 4 ]), further lead to the downgrade of supply chain performance at both enterprise level and regional level. With the rapid growth of logistics demand, the damage grows exponentially, which will eventually bring irreversible impacts to the economy and the whole ecosystem [ 5 ].

The operation management of physical distribution is one of the most significant and challenging sub-issues of the macro supply chain management (SCM) [ 6 ], because it involves real-time scheduling and coordination of hundreds of thousands of packages and containerized goods under a dynamic logistics scenario [ 7 ]. G&SL is defined as the planning, control, management, and implementation of logistics system through the advanced logistics technologies and environmental management, aiming to reduce pollutant emissions and improve logistics efficiency [ 8 ]. G&SL is not only concerned with providing customers with green products or services [ 9 ], but also with the green and sustainability of the entire lifecycle of the logistics process [ 10 ]. Various green logistics modes, activities, and behaviors were proposed and gradually realized from government rules to technological innovations. For example, the construction of green logistics network [ 11 , 12 , 13 ], reverse logistics [ 14 ], emission control [ 15 ], electric freight vehicle [ 16 ], modal shift and multimodal transportation [ 17 ], energy efficiency [ 18 ], collaboration [ 19 , 20 ], outsourcing [ 21 , 22 ], etc. A wide range of topics related to G&SL yielded substantial academic results and considerable practical performance. However, G&SL is still in its infancy and is far from meeting the challenges posed by the complexity of internal cooperation and uncertainties of external markets [ 1 ].

Previous studies reviewed G&SL from different perspectives. By reviewing 115 papers, Zhang et al. [ 10 ] analyzed the combinatorial optimization problems and swarm intelligence technique applied in improving G&SL performance. Qaiser et al. [ 23 ] conducted some brief statistics on the bibliometric information of 40 papers on G&SL. Bask and Rajahonka [ 8 ] mainly reviewed the role of environmental sustainability in multimodal freight transport decision-making. Based on 56 papers, Mangiaracina et al. [ 24 ] summarized the impact of business-to-customer transportation process on the environment. Arvidsson et al. [ 25 ] reviewed the sustainable measures for improving urban distribution efficiency. Pourhejazy and Kwon [ 26 ] conducted a survey on 380 articles published from 2005–2016 and revealed the application status of operations research technique in the supply chain optimization. The literature of green SCM was classified and reviewed by Srivastava [ 4 ] from a reverse logistics angle. This work was further enriched by Fahimnia et al. [ 27 ], who investigated the bibliographical information and trend of a majority of green SCM research through article co-citation network and keywords co-occurrence network.

However, based on the time of publication and the number of papers contained, the existing studies are outdated and incomplete, unable to provide a comprehensive analysis of the booming G&SL research in the past two years. Also, it is more difficult to integrate the multitudinous research directions to build a complete knowledge structure for G&SL. Therefore, it is of great theoretical and practical significance to objectively and quantitatively investigate the overall progress of G&SL.

This study aims to conduct a comprehensive review of the global G&SL literature, so as to explore the state-of-the-art, hotspots and research trend, as well as to build the G&SL knowledge classification system. Specifically, first, tracking and analyzing the evolution of the G&SL research field from (i) publication year and journals; (ii) countries, regions, and organizations; (iii) influential documents; (iv) keywords clustering and research themes. Second, establishing the knowledge taxonomy based on the scientometric results. Third, identifying the research gaps and the future research opportunities.

The novelty of this study lies in two aspects. One is to integrate the science mapping approach into the systematic literature review process to visualize the relationships among the G&SL literature. Science mapping approach is composed of data mining and bibliographic analysis, which can minimize subjective arbitrariness and grasp useful information to facilitate in-depth thematic analysis. Another is that this study further extends the bibliography to illuminate the emerging knowledge branches, gaps, and agendas in G&SL research, which will contribute to the improvement of G&SL practice and research innovation. The findings are expected to provide researchers and practitioners with a panoramic description and in-depth understanding of G&SL research. Additionally, the proposed knowledge structure can also be used as a handbook-like tool to further collect, analyze, and expand knowledge in the G&SL field and to provide references for other innovative logistics initiatives.

The rest is organized as follows. In Section 2 , the outline of research method is introduced. Section 3 presents the results of the data collection and the results of five parts of scientometric analysis. Section 4 proposes the taxonomy of G&SL research based on the keywords clustering and discusses the knowledge branches in detail. The current research gaps and agenda are also identified. Section 5 summarizes the major findings and limitations.

2. Research Method

2.1. overview of review protocol.

This review-based study conducted a systematic investigation on the academic development of global G&SL research with the aids of science mapping. Science mapping is a quantitative analysis approach that uses mathematical statistics and visualization techniques to study bibliographic networks (e.g., academics, institutions, themes, keywords, and journals) in a specific field [ 28 ]. This approach has been widely applied in many academic fields, such as sustainable transportation [ 29 ], environment science [ 30 ], city logistics [ 31 ] and waste management [ 32 ] and can directly synthesize salient findings from the existing knowledge system.

Figure 1 illustrates the detailed research process, consisting of three steps.

The flowchart of reviewing G&SL literature.

In step 1, the statistics was obtained after a comprehensive retrieval from two electronic databases, Web of Science (WoS) core and Scopus. Two rounds of selection were then performed to refine, classify, and encode the documents. The year publication trend, journal allocation and the most cited articles were described.

Four scientometric tests were carried out in step 2, namely (i) Journal co-citation analysis : to identify the most cited journals and the research domains they belong to. This analysis helps to reveal the distribution of published journals and cited journals of the reviewed documents, so as to identify popular journals in G&SL research domain. (ii) Countries/organizations collaboration analysis : to visualize the collaborative research network of G&SL among countries and organizations, so that the readers can quickly understand the partnerships between major research communities and institutions around the world. (iii) Document co-citation analysis : to highlight the influential G&SL articles and the corresponding reference relationships. By analysis of the papers with high citation, the emerging trend of scholars’ research interest to G&SL is easier to grasp. (iv) Keywords co-occurrence analysis : to map out the co-occurred time zone of the hotspots G&SL keywords and cluster them into several research themes. Network analysis of co-occurred keywords is used to clarify the knowledge structure of G&SL as well as to present the research hotspots and potential research opportunities in the future.

In step 3, the hierarchical knowledge structure of G&SL was proposed for thematic discussion.

The text mining software VOSviewer was adopted for science mapping, combining with another software CiteSpace to portray the time view of the clustered keywords based on the same data. VOSviewer, developed by van Eck and Waltman [ 33 ], is a comprehensive bibliometric analysis tool based on Visualization of Similarities (VOS) technology, which has unique advantages in clustering fragmented knowledge from different domains according to their similarity and relatedness. In the visualized networks, a node signifies a particular bibliographic item, such as organization, country, keyword or reference, etc. The node size represents the counting of the evaluated item namely citation or occurrence. Link denotes the co-citation, co-occurrence or collaboration relationship. The metric, total link strength (TLS), is outputted automatically by the software to reflect the correlation degree between any two nodes in the generated networks. A higher value of TLS, the higher importance and centrality of the item has [ 31 ]. Nodes with a high similarity were clustered together and distinguished by colors with other clusters, while the nodes with low similarity should be separated as far as possible. The similarity matrix can be calculated by Formula (1), where c ij is the co-occurred or co-cited times of item i and item j , W i and W j denote the node sizes of item i and item j respectively [ 33 ]. The stopping criterion of VOSviewer mapping is the minimal sum of weighted Euclidean distances of all items in each cluster [ 34 ], which can be expressed by Formula (2), where x i and x j are the positions of the nodes.

For a detailed operation manual of bibliographical experiments using science mapping approach, readers are advised to refer Jin et al. [ 28 ] and Hu et al. [ 31 ].

2.2. Literature Retrieval and Selection

The advanced retrieval function in Scopus and WoS core collection database was used to retrieve the G&SL related papers published during 1999 to August 2019 (see Table 1 ). To ensure the quality of the literature, the document types were restricted to research articles, while other types such as the conference proceeding, book chapter, letter or editorial material were excluded. The preliminary search yielded 1160 records. These records were imported into EndNote software for the first-round inspection to filter out duplicates and unqualified records in forms (e.g., article length and integrity). Additionally, those completely and partially irrelevant studies were removed. For example, an article entitled “Using logistics regression to analyze the sustainable procurement performance of large supply chain enterprises” was not the desired result. A total of 397 records were left after the first-round inspection. Then, the second-round selection was carried out by carefully reading the abstract of each document. The inclusion and exclusion criteria for this round focused on whether the document was consistent with the research topic, i.e., with green logistics initiatives, practices. and other G&SL innovations, rather than broader research, such as production, manufacturing or urban transportation. Unless it has a strong relation with G&SL. In particular, the following topics were excluded: (i) green design on the specialized logistics technology e.g., biomass and biofuel; (ii) business competition and (iii) offshoring and lean production. Finally, 91 records were removed, leaving 306 full-length articles in our review portfolio.

Results of literature retrieval and selection.

3. Scientometric Experiments and Analysis

3.1. chronological publication trend.

Figure 2 displays the number of papers published annually from 1999–2019 in the portfolio. Obviously, research on G&SL was virtually stagnant until 2009, and since 2010, it has increased significantly year by year. By 2018, a staggering 62 articles were searchable. The vigorous development of academic research indicates the expansion of the scope and branch of G&SL. Furthermore, from the publication number and the recent discussed topics of G&SL, it is evident that the public awareness, market acceptance, social demand and real-world practice of sustainable logistics measures are undergoing remarkable ascent.

Year profile of indexed documents.

3.2. Journal Allocation and Co-Citation Analysis

All 306 documents were found in 81 different journals. As shown in Figure 3 , the top 15 journals contributed 155 papers, accounting for 51% of the total. The impact factors of journals were also attached based on the Journal Citation Reports (2018). Sustainability ranks first (35, 11.4%), followed by Journal of Cleaner Production (24, 7.8%), Transportation Research Part D: Transport and Environment (17, 5.6%) and International Journal of Production Economics (13, 4.2%). Among the top 15 journals, eight are from UK, four from The Netherlands, two from Switzerland, and one from Germany. The papers are mainly distributed in the three academic fields of environment, traffic engineering and operations management, but they obviously account for a larger proportion in the environmental science and sustainable field, which is in line with the connotation of G&SL.

Rank of journals in G&SL publication number.

As shown in Figure 4 , among the 12,408 references (corresponding to 2349 different journals), a network of 46 items and 1025 links was formed by identifying the journals that had been cited more than 50 times. In general, the journals that influenced G&SL research are concentrated in three interrelated clusters. First is the operations research (OR), such as European Journal of Operational Research (TLS = 13,076, citation = 494), International Journal of Production Research (TLS = 252, citation = 8260), Expert Systems with Applications (TLS = 4748, citation = 153), Omega (TLS = 5139, citation = 150) and Computers & Operations Research (TLS = 4234, citation = 137), which can offer quantitative methods for the decision-making and optimization issues related to G&SL. The second cluster is transportation research (TR), such as Transportation Research Part A (TLS = 2057, citation = 103), Part D (TLS = 3883, citation = 176), Part E (TLS = 7576, citation = 260), and Journal of Transport Geography (TLS = 2089, citation = 91), which accumulates enormous knowledge towards transportation planning, technology and operations that can enlighten G&SL research from real-life transport demand and practice. The third cluster, including Supply Chain Management (TLS = 6546, citation = 232), International Journal of Physical Distribution & Logistics Management (TLS = 6357, citation = 235) and Journal of Business Logistics (TLS = 2837, citation = 96), etc., reveals that a large amount of G&SL research was conducted based on the research foundation of logistics and supply chain management (SCM). Among all the publications, Journal of Cleaner Production (TLS = 13,799, citation = 555) and International Journal of Production Economics (TLS = 13,903, citation = 495) are the two most co-cited journals. They often act as hubs, integrating the results of OR, TR and SCM with social, environment or economic implications to provide cross-domain knowledge crucial to the diverse development of G&SL.

Mapping of the journals co-cited.

3.3. Countries/Organizations Collaboration Analysis

Table 2 lists the countries or regions that are actively studying G&SL, showing six measurements, including number of publications (NP), TLS, average citation year, total citations, average citation per country/region, and average normalized citation. The average normalized citation was calculated by dividing the total number of citations by the average number of citations published per year [ 34 ]. Figure 5 displays the collaboration network among countries and regions. The minimum number of documents and citations for a country was set at 5 and 30 respectively. Finally, a map with 25 items and 58 links was generated.

Mapping of countries/regions contribute to G&SL research.

Summaries of countries/regions active in G&LS research.

According to Table 2 , G&SL research is widely distributed, especially in Europe, Asia, and North America, which is a field of worldwide concern. Mainland China has the most publications, but the United States has the highest total citation. Other countries/regions such as Italy, Singapore, Hong Kong, and Taiwan present a lower number of publications; however, they keep significant figures of average normalized citation which can strongly express their high influence. Besides, most of the documents contributed by these countries/regions were published in the last three years, which means they are playing an increasingly active role in promoting G&SL.

Two evidence can be observed from Figure 5 . First, based on a partnership, the global G&SL research is divided into four communities. Therein, two communities are leaded by European counties, such as UK, Spain, The Netherlands, and Italy, while the other two communities are “Mainland China-Hong Kong-Singapore” and “United States-India-Australia-Portugal-Taiwan”, dominated by China and USA, respectively.

Second, the international collaboration is not significant. Taking mainland China for instance, about 70 percent of 49 publications are completed entirely by domestic institutions. The Swedish publications do not have any co-authors from other countries or regions. This phenomenon may be due to the large differences in the background and model of G&SL development in different countries [ 35 ]. Moreover, the knowledge gap caused by the wide extension of G&SL and the scattered knowledge structure make the research still focus on the respective fields of researchers, such as sustainable development [ 36 ], environment governance [ 37 ] and transportation planning [ 38 ]. Therefore, at present, the cooperation between academic institutions of different backgrounds has not been widely carried out.

Among the 402 organizations that contributed to G&SL research, those with more than five documents and over 30 citations were built into a network of 22 items and 22 links, as shown in Figure 6 . None of the organizations published more than 10 papers (3% of 306) and the studies were relatively independent. Therefore, it can be argued that no organization has yet been able to lead G&SL research so far. However, some of the institutions located in Asia Pacific and Europe have a higher reputation in G&SL due to higher citations, including the Hong Kong Polytechnic university (Hong Kong, 388 citations), Wageningen University (The Netherlands, 370 citations), Aristotle University of Thessaloniki (Greece, 324 citations), National Chiao Tung University (Taiwan, 330 citations), Iowa State University (USA, 206 citations), University California Berkeley (USA, 160 citations) and Nanyang Technological University (Singapore, 137 citations). In addition, Figure 6 also shows insufficient collaborative research across organizations.

Mapping of global collaboration network among organizations.

3.4. Influential Research Highlight

Through the document co-citation test of the portfolio, the most influential G&SL publications in the past two decades were analyzed and the co-citation network was constructed. In VOSviewer, the minimum number of citations was set to 30 to build a co-cited visual network map of 83 items and 350, as shown in Figure 7 . The nodes in the map denote the documents that were identified by the first author name and the publication year. The colors of the nodes and the links represent the time of publication and the time of two documents that are co-cited, respectively. The co-occurrence of the literature shows an obvious type of “local concentration and overall dispersion”, indicating that some G&SL studies were widely recognized and produced some common ideas and results. Most papers with high citation appeared around 2010, which was a landmark year for G&SL research. The co-citation time series indicate that G&SL knowledge spreads faster and faster.

Mapping of the influential documents and their co-citation relationship.

The top 15 most cited papers are presented in Table 3 , showing their publication year, title, TLS, citation counts and topics. The most cited study was by Dekker et al. [ 39 ], one of the first methodological studies to link the operations research knowledge (such as design, planning, and control) to the field of green logistics. The second is Sheu et al. [ 40 ], whose main contribution is to propose a modeling technique for sustainable logistics operations and management decisions to maximize supply chain profits. These were followed by papers by Lai and Wong [ 41 ] and Ubeda et al. [ 42 ], which focused on using the scenario-based approaches, such as the questionnaire and case study, to evaluate the environmental performance of green logistics practices. The main topics of other highlighted documents involve: (i) management insights from industrial practices [ 43 , 44 ]; (ii) multi-criteria evaluation system for green logistics (e.g., policy [ 45 ], environment [ 46 ], and transportation planning [ 47 ]); (iii) network facilities design and optimization [ 48 , 49 ]; (iv) reverse logistics [ 50 , 51 ]; and (v) enterprise responsibility and third-party logistics [ 52 ].

List of publications with the highest impact in G&SL.

3.5. Keywords Co-Occurrence Analysis

The keywords co-occurrence analysis was conducted to describe the internal composition and structure of G&SL and to reveal the frontiers [ 31 ]. The options “All Keywords” and “Full Counting” in VOSviewer analysis were checked to obtain a holistic intellectual landscape of G&SL research. Before the scientometric test, the keywords, such as “third-party logistics providers” versus “3PL”, “transport” versus “transportation”, which are necessary due to differences in expression, were manually simplified on the original data file. The minimum occurrences of each keyword was set to 4, forming a network of 112 nodes representing keywords (1455 keywords in all documents) and 2067 links, as shown in Figure 8 .

Mapping of co-occurred keywords.

Figure 8 displays the mainstream of research keywords in G&SL and their co-occurrence relationships. Divide these keywords into four clusters and distinguish them with different colors. Therein, Cluster #1 contains 18 items focusing on the practice and management of logistics sustainability (e.g., collaboration, case study and intermodal transportation), while Cluster #2 covers 25 items, concentrating on the environmental issues of freight transport, such as carbon emission, energy consumption and lifecycle assessment. Cluster #3 (34 items) and Cluster #4 (34 items) emphasize on the “model, planning and optimization” as well as the “supply chain performance, development strategy and competitiveness”, respectively.

Table 4 shows the detailed information of the significant keywords. The top 10 most frequently studied and highly connected terms are sustainability (Feq. = 80, TSL = 547), green supply chain (Feq. = 68, TSL = 629), management (Feq. = 58, TSL = 411), model (Feq. = 55, TSL = 394), green logistics (Feq. = 48, TSL = 325), performance (Feq. = 47, TSL = 367), logistics (Feq. = 46, TSL = 299), framework (Feq. = 43, TSL = 356), impact (Feq. = 41, TSL = 312) and reverse logistics (Feq. = 39, TSL = 323). These keywords play a critical role in forming G&SL research topics and connecting major branches of knowledge. According to the metric of average citations, the following keywords, including transportation, environmental sustainability, production, reverse logistics, and efficiency, aroused a lot of attention.

Summaries of significant keywords and theme clusters of G&SL research.

Keyword co-occurrence network is a static expression of a particular area that does not take into account changes over time in the manner that the terms are used [ 54 ]. Figure 9 shows a time zone view of keywords that occur more than eight from 1999 to 2019. Each term is arranged in chronological order to present the trend and interaction of keywords. Studies on management, model and green supply chains had been published extensively before 2005 and had been going for a long time, showing that these early topics are still the hotspots of current research. In contrast, articles related to collaboration, transportation planning, modal shift and stakeholder were published from 2015 to 2017, which are emerging themes discussed frequently in recent years and may become the hotspots of future research. Additionally, a large proportion of the keywords were proposed between 2007 and 2015, indicating that G&SL research was greatly enriched during this period. Table 4 presents the time span of all highlighted keywords.

A time zone view of clustered research themes: 1999-2019.

4. Discussion

4.1. knowledge taxonomy of current research.

Through the aforementioned analysis, the research progress, evolutionary trend, and hot-discussed topics of global G&SL are clarified. However, the generic scientometric results cannot accurately reflect the explicit division of the multifarious knowledge of a domain [ 31 ]. Based on the clustering analysis of high-frequency keywords, a comprehensive taxonomy of G&SL knowledge from 1999 to 2019 was further proposed, and each separated branch was thematically discussed in-depth subsequently. Topics with similar attributes were integrated into different categories of themes and manually renamed to make the taxonomy more compact and easy to understand. Figure 10 demonstrates the mind mapping of G&SL research themes, where a total of 5 alignments and 50 sub-branches are assembled. The number of representative articles of each theme was also attached.

The knowledge taxonomy of G&SL themes.

4.1.1. Evaluation on the Social, Environmental and Economic Impacts of G&SL Initiatives

Nearly a quarter of the literature (71 out of 306 papers) focused on evaluating and quantifying how the potential green logistics initiatives improve the “triple bottom line” (i.e., social, environmental and economic performance, SEE) of existing freight activities. The subjects of these studies were basically originated from four aspects: carbon emission, energy consumption, social sustainability, and external cost-and-benefit. Mattila and Antikainen [ 15 ] provided a backcasting method for the long-term prediction of greenhouse gas emissions and fossil fuel consumption in long-distance freight transport, considering the sustainable goals and policies developed by the EU governments. Similar research was conducted for assessing the U.S. scenario [ 46 ]. A questionnaire survey conducted by Makan and Heyns [ 55 ] found that the pressures from consumer, brand protection, top management, and cost-saving and revenue are the major drivers for freight organizations to implement the sustainable initiatives. Khan et al. [ 56 ] modeled the impact of G&SL performance on the countries’ economic development and macro-level social and environmental indicators. Papoutsis et al. [ 57 ] and Solomon et al. [ 58 ] both maintained that logistics sustainability is closely related to operational efficiency and social acceptance from an economic and environmental perspective. Through the expert scoring, Morana and Gonzalez-Feliu [ 59 ] identified the most prominent factors affecting the sustainability of urban logistics are monetary saving, services quality, and customers’ satisfaction rate (economic), pollution emissions and congestions (environmental), and the number of employment created/destroyed (social). Social and environmental activities play a more important role in promoting sustainable logistics than financial-economic activities [ 60 ]. Rashidi and Cullinane [ 61 ] found that the national logistics industry with high SEE index has the following features: (i) well-planned logistics network infrastructure; (ii) high quality of service operators; (iii) shipments tracing technology; and (iv) efficient timetable scheduling.

Another part of emphasis was given to SEE performance of G&SL based on logistics operations and business. Guo and Ma [ 62 ] evaluated the energy consumption and emission level under different logistics business modes, concluding that the third-party logistics provider and the joint distribution modes have obvious environmental advantages in developing green urban distribution. Wang et al. [ 63 ] found that green logistics performance would impose positive effects to the exporting countries in the international trade. Herold and Lee [ 64 ] investigated the carbon reports disclosed by some giant international logistics enterprises, e.g., UPS, FedX and DHL, and compared their sustainability-related strategies, namely legitimacy-seeking arguments versus energy and emission reduction. In addition, a variety of qualitative analysis measures, such as fuzzy multi-criteria evaluation modeling [ 65 ], data envelopment analysis [ 66 ], and analytic hierarchy processes [ 67 ] were also widely applied to illuminate the logic between SEE performance and G&SL.

Except for the three-dimensional evaluation system, some scholars also analyzed the critical success factors and barriers for G&SL initiative implementation from the SEE perspectives. For instance, Arslan and Sar [ 68 ] found that the managers’ intention towards green logistics initiatives is generally determined by the environmental attitude, perceived behavior control and subjective norm. Besides, government subsidizes [ 69 ] and internalization of externalities [ 70 , 71 ] were considered to be the effective models to reduce negative external cost in the logistics industry, thus promoting the greening process of the logistics market.

4.1.2. Planning, Policy and Management Research of G&SL

This knowledge branch focuses on two basic G&SL topics, (i) the planning, development, and policymaking from industrial level, and (ii) the collaboration strategy and management from project level. For the former, Lindholm and Blinge [ 2 ] indicated that the public support, stakeholder partnership, and excellent management skills are the most significant factors to achieve sustainable development of the logistics industry. The coordination among metropolitan economy, logistics infrastructure investment, and industrial chain upgrading is the essential foundation of G&SL [ 36 ]. Integrating freight activities into the general planning procedure or transport planning is also considered important for the implementation of G&SL. Shankar et al. [ 72 ] quantified the dynamic uncertainties and intrinsic sustainability risks of freight transport and stated that most of the risks were socially induced rather than financially driven. The risks of multimodal green logistics were analyzed by Kengpol and Tuammee [ 73 ]. A system dynamics simulation conducted by Sudarto et al. [ 74 ] revealed that the economic performance of G&SL is directly affected by freight policy, while environmental performance is indirectly affected. Klumpp [ 75 ] proposed two strategies to develop green logistics, namely encouraging public investment and imposing heavy taxes on carbon raw materials.

For the latter, the collaboration and game among logistics service providers (LSP), government, shippers, and enterprises are paid more attention. Commonly, a positive cooperation strategy of stakeholders will significantly improve the operational performance of G&SL [ 76 ] and even the entire supply chain [ 19 ]. Therein, the benefits brought by the collaboration between suppliers and customers [ 77 ] and LSPs-shippers [ 78 ] are particularly salient. The government plays a dominant role in the knowledge dissemination [ 79 ] and economic incentive of greenization [ 20 ], leading to the innovation of logistics technology. Moreover, the shippers’ willingness to pay for G&SL products [ 80 ], the exploitation of green logistics knowledge [ 81 ], as well as the gaps between green logistics demand and supply [ 82 ] also aroused research attention.

Furthermore, several novel business and operational modes of logistics aiming at improving the sustainability in transportation process were proposed, e.g., freight consolidation [ 83 ], smart logistics [ 22 ], and low emissions zones [ 84 ]. The most hotly debated topics are outsourcing and crowd shipping (CS). CS, proposed for the last-mile delivery problem, is a concept that means the parcels and passengers are co-transported along a passenger trip [ 85 ]. According to Ameknassi et al. [ 86 ], freight transportation, warehousing, and reverse logistics are the three major outsourced logistics activities. The outsourcing strategy has proven to be advantageous in reducing energy use, global warming, and supply chain risk, compared with common logistics operations [ 87 ].

4.1.3. Real-World Application Areas and Practices

Over the past decade, research on the G&SL practices were carried out over a broad range, including SCM, reverse logistics (RL), e-commerce, urban distribution, multimodal transport, and other dedicated logistics such as food [ 88 ] and manufacturing [ 89 ]. Much valuable experience and instructions can be obtained from real-world applications. For example, the unsustainability of the supply chain is largely due to the poor logistics practices in the downstream [ 90 ], which specifically refers to transport operation delay [ 91 ], poor communication [ 91 ] and the lack of effective management of carbon footprint [ 92 ]. A sustainable SCM is an effective measure to improve the competitiveness, financial and environmental performance of logistics enterprises. However, this is not absolute, Hazen et al. [ 93 ] believe that some green SCM practices might not necessarily lead to competitive advantage, but make users feel that they are getting low-quality products.

Reverse logistics is convincingly one of the most efficient solutions to reduce environmental pollution and waste of resources by capturing and recovering the values of the used products [ 94 ]. Legislation, social image, corporate citizenship, and market competence force enterprises to integrate RL into their supply chains [ 95 ]. In real-world application, improving RL sustainability and greening process is the primary goal to optimize the overall supply chain performance. Our review found that most green-related RL studies focused on the network design [ 96 ] and system planning [ 14 ]. Other topics are waste recycling management [ 97 ], benefits assessment [ 98 ], reverse operations outsourcing [ 99 ] and social responsibility [ 50 ].

The unsustainability of urban logistics makes it the most urgent goal of greening. Huge logistics demand, such as rapid business-to-business and business-to-customer logistics activities, make freight transportation in big cities face the dilemma of air pollution, poor accessibility, and livability [ 31 ]. The practice of integrating green logistics planning into smart cities construction has been carried out for a long time, especially in Europe, mainly including last-mile delivery [ 100 ], traffic management [ 101 ] and lean logistics [ 102 ].

Compared with G&SL in urban domain, the sustainability issues regarding inter-city or regional logistics are more emphasized on the intermodal application. The shift of road-based modal to other transportation system, such as rail and water has the potentials of ensuring environmental sustainability, flexibility, and cost reduction [ 17 ]. However, despite the encouragement by the government, the practice of intermodal transport is still in a preliminary stage due to the difficulties of infrastructure investment [ 103 ].

4.1.4. Emerging Technologies Proposed for G&SL Development

Developing advanced facilities and technologies is a sustainable and forward-looking solution to meet the challenge of freight transport. Many emerging logistics systems were proposed in recent years. Such as urban consolidation center [ 104 ], electric road system [ 105 ], intelligent transportation system [ 106 ] and packaging benchmarking system [ 107 ], etc. Meanwhile, some soft applied techniques, such as big data [ 108 ], internet of things [ 109 ] and cloud computing platform [ 110 ], have also been applied to logistics operations to support the sustainable development of the emerging systems.

Electric vehicles (EVs) technology, which has been widely applied in passenger transport, is also waving a revolution in the field of G&SL. Current research on freight EVs mostly focuses on energy efficiency [ 111 ], fleet optimization [ 16 ] and environmental benefits [ 112 ]. Simulation results from various cities show that EVs achieve extremely high benefits in carbon emission reduction, with over 80% relief rate tested by Giordano et al. [ 112 ].

For reducing the negative externalities such as traffic congestion and disturbance, another interesting concept, i.e., transferring the ground logistics process to underground space, namely the Underground Logistics System (ULS), has aroused increasing attention. ULS refers to using a group of hierarchical underground nodes, pipelines, and tunnels to distribute cargo flows in and between cities with 24-h automated operations [ 113 ]. ULS can be designed as a network form connecting urban logistics parks and last-mile delivery, or a dedicated underground container line established between seaports and urban gateways, leading to huge environmental and social benefits (e.g., energy-saving, accidents and congestion mitigation and improving urban logistics capacity, etc.) [ 114 ]. So far, the technological feasibility of several ULS projects was acknowledged, yet the large-scale implementation has not started due to the relatively high construction cost and low public awareness [ 5 ]. For this reason, the collaborative strategy of retrofitting existing urban rail transit systems, such as trams, light rail or subways, to achieve mixed passenger-and-freight transport has received higher recognition and was successfully stepped into engineering practice in some European cities [ 115 ]. Compared with ULS, the collaborative modes are easier to implement, since the dual use of transportation infrastructures would moderate the system cost to an acceptable level [ 49 , 116 ].

4.1.5. Operations Research and Optimization Methods for G&SL Decision-Making

The operations research (OR) of G&SL issues that are originated from real-world applications is always being a well-concerned topic because it is directly related to the quality of some critical decision-making in logistics operation. The OR method applied for G&SL is defined as a better of science to identify the trade-offs between environmental aspects and costs, so that the corresponding decisions such as location, transportation, warehousing, and inventory can be optimized and the limited resources can be reasonably assigned [ 39 ]. Dekker et al. [ 39 ] classified the application of OR in green logistics as follows: logistics services network design [ 48 ], facility location [ 117 ], vehicle routing problem [ 118 ], inventory management [ 40 ] lifecycle production optimization [ 119 ], supply chain planning, control, and procurement [ 120 , 121 ] and model choice [ 122 ]. A variety of OR techniques, such as heuristic algorithms [ 121 ], stochastic programming [ 53 ], and robust optimization [ 123 ], were developed for the above issues. In addition to the objectives of general logistics planning e.g., cost and efficiency, the G&SL version focus more on the minimization of environmental influence, e.g., carbon emission and energy consumption. Currently, OR is increasingly applied to optimize the G&SLs’ decision-making in a complex scenario set, such as demand uncertainty [ 48 ] and facilities failure [ 124 ].

4.2. Research Gaps and Agenda

Through the above scientometric analysis and thematic discussion, the comprehensive research trend, mainstream academic topics, and knowledge taxonomy of G&SL domain were revealed. Although researchers and practitioners achieved substantial results in promoting G&SL theory and practice, there are still some shortcomings that need to be elaborated in future studies.

4.2.1. Limitations of Global Collaboration and General Evaluation Framework

In terms of research model, international cooperation is still lacking. The broad applicability of most G&SL knowledge based on local cases deserves further discussion, such as planning methods and evaluation systems. European countries made great efforts in rebuilding the integration of green logistics. However, the lack of international cooperation and universal solutions hinders the dissemination and deepening of knowledge, and the current achievements are far from enough to promote the globalization of G&SL, which is reflected in the imbalance of global G&SL practice.

To fill this gap, although it is recognized that logistics policy has a strong regional character, cross-institutional and cross-national collaborative research on market operation, industrial metrics, technology innovation and macro development strategies should be strengthened under the trend of supply chain globalization. For example, more attention can be paid to the horizontal comparison of green logistics mode, scheme and performance under different case backgrounds. Additionally, more empirical studies are needed to be carried out in some developing countries in Asia and elsewhere in the world, considering they are the fast growing economies with higher population and logistics demand.

4.2.2. Complement Research from a Global/Holistic Perspective

Although the knowledge branch of research is flourishing, it is acknowledged that there is still a need to supplement the overall or holistic research to improve the knowledge system of G&SL. Research on sustainability and green has always been complex and multi-variable, interactive, with far-reaching implications. Besides, sustainability and green are public and social issues. Current theoretical applications are limited to the analysis of local or one-way relationships, such as LSP/retailer/carrier responses to green policies, planning and performance evaluation of green and sustainable initiatives.

The operation and decision-making of G&SL involve many stakeholders, such as local authorities, manufactures, LSP, carriers, customers, and even the sharers of transportation resources. The impact of G&SL should also be long-term and dynamic. Thus, the whole picture includes multiple perspectives, such as the dynamic evaluation of the whole life-cycle of green logistics practice, the decision interaction among multiple stakeholders, and the follow-up research and report on a new green technology or practice.

4.2.3. Lack of Effective Platform to Accelerate the Research of Innovation Technology

Without green innovation technologies, the effect of implementing G&SL from a management perspective alone is minimal. However, it takes a lot of time for some innovative technologies that can fundamentally improve the negative effects of logistics to move from laboratory to application. Applications such as the EV took decades to implement [ 125 ]. Although the technology is constantly updated and improved, more management lag. Another competent concept, the ULS, ASCE has published a feasible technical system as early as 1998 [ 126 ], but only in a few countries has it been publicly piloted in recent years.

The introduction of a new thing does require a long period of demonstration, such as the reliability of the technology, the acceptability of the market and the ambiguity of the real benefits. However, the problem is often the gap and lag in the research of application management in the transition from technical problems to market application and practice management. Therefore, building effective platforms based on multidisciplinary, cross-organizational collaboration to accelerate the research and application of innovative technologies is particularly important for G&SL practices, such as ULS, RL, and CS. Such calls are all the more urgent in their own research.

5. Conclusions

The concept of green and sustainable logistics has received increasing attention and consideration government sectors, scholars, practitioners, and international organizations. A large amount of practical achievement was made at both the industrial and theoretical levels. This study reviewed 306 valuable contributions regarding G&LS over the past two decades through a three-step review program. They were described in year publication, journal allocation and citation counts. Then, the bibliographic networks of countries, organizations, journal and document co-citations, keyword co-occurrence and timezone clusters of research themes were visualized to help understand the overall research status and academic progress worldwide. Grounded in the scientometric analysis, an integrated knowledge taxonomy of the G&SL field was presented, including five major alignments and 50 sub-branches.

Results indicate that the chronological publication of G&SL shows a trend of rapid increase. The quantity of literature published in 2018 is fifteen times more than that of 10 years ago. Sustainability , Journal of Cleaner Production , Transportation Research Part D: Transport Environment and International Journal of Production Economy are the top four journals, which contributed over a quarter of all G&SL papers since 1999. The maps of journal allocation and co-cited journals show that the current research is most relevant to the environmental science and transportation science. In terms of countries, China, the United States, the UK, Sweden, and India are the major territories of G&SL research. The network across co-authored organizations and countries revealed that the collaboration among different research communities is not strong. Hence an active and robust global collaboration atmosphere has not formed yet.

The map of co-occurred keywords showed that the most frequently discussed G&SL themes in each cluster were sustainability and management (cluster #1), freight transportation and carbon emission (cluster #2), model and reverse logistics (cluster #3), and green supply chain and green logistics (cluster #4). The timezone view of keywords showed that articles related to collaboration, transportation planning, modal shift and stakeholder were largely published during the recent years. On this basis, the knowledge taxonomy of G&SL was manually synthesized from five aspects: (i) evaluation on SEE impacts of G&SL initiatives; (ii) planning, policy, and management research; (iii) real-world application areas and practices; (iv) emerging technologies and (v) operations research and optimization methods for G&SL decision-making.

Finally, the potential roadmap for filling current research gaps was recommended, which were divided into three streams: (i) more global research collaboration should be advocated to jointly develop and supplement the comprehensive evaluation framework of G&SL performance; (ii) future research efforts could focus on the interactive and dynamic relationships among sustainable development goals, green policies and the decision-making of multiple stakeholders; (iii) the application-oriented platforms and management research for some most advanced green logistics initiatives would be highly beneficial in promoting G&SL innovation.

However, it should be noted that the data used in this study was confined to those research articles and review articles that were published in the peer-reviewed journals, and they were retrieved only from the two mainstream databases considering the applicability of software. Although the indexed documents could represent most of the convictive viewpoints of G&SL research, some valuable articles that were published in other forms or included in other databases might be overlooked inevitably. To sum up, this review has great room for improvement in terms of material selection. A systematic investigation incorporating valuable conference proceedings, reports, and books in the field of green logistics or green supply chain is expected to portray a more comprehensive knowledge map for future research. Additionally, the in-depth review of the hotspot themes in G&SL domain e.g., OR application and SCM, may also contribute to multidisciplinary integration and interaction.

Acknowledgments

The editors and anonymous reviewers of this paper are acknowledged for their constructive comments and suggestions.

Author Contributions

R.R. and W.H. proposed the research framework, analyzed the data and wrote the article; B.S. and Y.C. contributed to data collection; J.D. and Z.C. contributed to revising article. All authors have read and agreed to the published version of the manuscript.

This work was supported by the National Natural Science Foundation of China (Grants no. 71631007, no. 71601095 and no. 51478463), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant no. SJCX19_0230).

Conflicts of Interest

The authors declared that they have no conflicts of interest to this work.

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Green transportation and the role of operations research

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Smart and Sustainable Cities Conference

SSC 2018: Green Technologies and Infrastructure to Enhance Urban Ecosystem Services pp 249–268 Cite as

Green and Resilient City: Obligatory Requirements and Voluntary Actions in Moscow

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The interest to the sustainable development, resilience and smartness of cities and communities has been growing globally since 1980s. City governments have been working out strategies, forming unions and associations, and exchanging experience in facing urbanization challenges and managing city assets sustainably. Authors consider international initiatives and standards providing for the common background needed to work out and implement sustainable development and resilience strategies and management plans as well as to assess and compare results achieved. Major initiatives analyzed include the United Nations HABITAT Program, the International “Green City Index” research, the network of the world’s megacities committed to addressing climate change (C40), the Charter of European Cities and Towns Towards Sustainability (Aalborg Charter) and the Working Group on Environmentally Sustainable Cities of Association of Southeast Asian Nations. A new series of the International Organization for Standardization standards ISO 37000 establishing requirements to management systems for sustainable development of communities and offering guidance in setting aims and objectives and measuring success is considered. Peculiarities of the understanding and use of these standards in Russia are described. Authors study a wide range of legal requirements set by Moscow city government in the period of 1993–2018 and demonstrate advantages and shortcoming of the legal acts passed and enforced. Consider voluntary actions undertaken by the local community, non-governmental organizations and educational establishments. The Chapter demonstrates the need for systematizing patchy policy documents and research projects. The case for the restoration of Moscow water bodies (small rivers) as backbones of the urban ecological network will be elaborated.

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Vakula, M.A., Guseva, T.V., Tikhonova, I.O., Molchanova, Y.P., Schelchkov, K.A. (2020). Green and Resilient City: Obligatory Requirements and Voluntary Actions in Moscow. In: Vasenev, V., Dovletyarova, E., Cheng, Z., Valentini, R., Calfapietra, C. (eds) Green Technologies and Infrastructure to Enhance Urban Ecosystem Services. SSC 2018. Springer Geography. Springer, Cham. https://doi.org/10.1007/978-3-030-16091-3_27

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Original research article, impact of industrial policy on urban green innovation: empirical evidence of china’s national high-tech zones based on double machine learning.

• College of Economics and Management, Taiyuan University of Technology, Taiyuan, China

Effective industrial policies need to be implemented, particularly aligning with environmental protection goals to drive the high-quality growth of China’s economy in the new era. Setting up national high-tech zones falls under the purview of both regional and industrial policies. Using panel data from 163 prefecture-level cities in China from 2007 to 2019, this paper empirically analyzes the impact of national high-tech zones on the level of urban green innovation and its underlying mechanisms. It utilizes the national high-tech zones as a quasi-natural experiment and employs a double machine learning model. The study findings reveal that the policy for national high-tech zones greatly enhances urban green innovation. This conclusion remains consistent even after adjusting the measurement method, empirical samples, and controlling for other policy interferences. The findings from the heterogeneity analysis reveal that the impact of the national high-tech zone policy on green innovation exhibits significant regional heterogeneity, with a particularly significant effect in the central and western regions. Among cities, there is a notable push for green innovation levels in second-tier, third-tier, and fourth-tier cities. The moderating effect results indicate that, at the current stage of development, transportation infrastructure primarily exerts a negative moderating effect on how the national high-tech zone policy impacts the level of urban green innovation. This research provides robust empirical evidence for informing the optimization of the industrial policy of China and the establishment of a future ecological civilization system.

1 Introduction

The Chinese economy currently focuses on high-quality development rather than quick growth. The traditional demographic and resource advantages gradually diminish, making the earlier crude development model reliant on excessive resource input and consumption unsustainable. Simultaneously, resource impoverishment, environmental pollution, and carbon emissions are growing more severe ( Wang F. et al., 2022 ). Consequently, pursuing a mutually beneficial equilibrium between the economy and the environment has emerged as a critical concern in China’s economic growth. Green innovation, the integration of innovation with sustainability development ideas, is progressively gaining significance within the framework of reshaping China’s economic development strategy and addressing the challenges associated with resource and environmental limitations. In light of the present circumstances, and with the objectives outlined in the “3060 Plan” for carbon peak and carbon neutral, the pursuit of a green and innovative development trajectory, emphasizing heightened innovation alongside environ-mental preservation, has emerged as a pivotal concern within the context of China’s contemporary economic progress.

Industrial policy is pivotal in government intervention within market-driven resource allocation and correcting structural disparities. The government orchestrates this initiative to bolster industrial expansion and operational effectiveness. In contrast to Western industrial policies, those in China are predominantly crafted within the administrative framework and promulgated through administrative regulations. Over an extended period, numerous industrial policies have been devised in response to regional disparities in industrial development. These policies aim to identify new growth opportunities in diverse regions, focusing on optimizing and upgrading industrial structures. These strategies have been implemented at various administrative levels, from the central government to local authorities ( Sun and Sun, 2015 ). As a distinctive regional economic policy in China, the national high-tech zone represents one of the foremost supportive measures a city can acquire at the national level. Its crucial role involves facilitating the dissemination and advancement of regional economic growth. Over more than three decades, it has evolved into the primary platform through which China executes its strategy of concentrating on high-tech industries and fostering development driven by innovation. Concurrently, the national high-tech zone, operating as a geographically focused policy customized for a specific region ( Cao, 2019 ), enhances the precision of policy support for the industries under its purview, covering a more limited range of municipalities, counties, and regions. Contrasting with conventional regional industrial policies, the industry-focused policy within national high-tech zones prioritizes comprehensive resource allocation advice and economic foundations to maximize synergy and promote the long-term sustainable growth of the regional economy, and this represents a significant paradigm shift in location-based policies within the framework of carrying out the new development idea. Its inception embodies a combination of central authorization, high-level strategic planning, local grassroots decision-making, and innovative system development. In recent years, driven by the objective of dual carbon, national high-tech have proactively promoted environmentally friendly innovation. Nevertheless, given the proliferation of new industrial policies and the escalating complexity of the policy framework, has the setting up of national high-tech zones genuinely elevated the level of urban green innovation in contrast to conventional regional industrial policies? What are the underlying mechanisms? Simultaneously, concerning the variations among different cities, have the industrial policy tools within the national high-tech zones been employed judiciously and adaptable? What are the concrete practical outcomes? Investigating these matters has emerged as a significant subject requiring resolution by government, industry and academia.

2 Literature review and research hypothesis

2.1 literature review.

When considering industrial policy, the setting up national high-tech zones embodies the intersection of regional and industrial policies. Domestic and international academic research concerning setting up national high-tech zones primarily centers on economic activities and innovation. Notably, the economic impact of national high-tech zones encompasses a wide range of factors, including their influence on total factor productivity ( Tan and Zhang, 2018 ; Wang and Liu, 2023 ), foreign trade ( Alder et al., 2016 ), industrial structure upgrades ( Yuan and Zhu, 2018 ), and economic growth ( Liu and Zhao, 2015 ; Huang and Fernández-Maldonado, 2016 ; Wang Z. et al., 2022 ). Regarding innovation, numerous researchers have confirmed the positive effects of national high-tech zones on company innovation ( Vásquez-Urriago et al., 2014 ; Díez-Vial and Fernández-Olmos, 2017 ; Wang and Xu, 2020 ); Nevertheless, a few scholars have disagreed on this matter ( Hong et al., 2016 ; Sosnovskikh, 2017 ). In general, the consensus among scholars is that setting up high-tech national zones fosters regional innovation significantly. This consensus is supported by various aspects of innovation, including innovation efficiency ( Park and Lee, 2004 ; Chandrashekar and Bala Subrahmanya, 2017 ), agglomeration effect ( De Beule and Van Beveren, 2012 ), innovation capability ( Yang and Guo, 2020 ), among other relevant dimensions. The existing literature predominantly delves into the correlation between the setting up of national high-tech zones, innovation, and economic significance. However, the rise of digital economic developments, notably industrial digitization, has accentuated the limitations of the traditional innovation paradigm. These shortcomings, such as the inadequate exploration of the social importance and sustainability of innovation, have become apparent in recent years. As the primary driver of sustainable development, green innovation represents a potent avenue for achieving economic benefits and environmental value ( Weber et al., 2014 ). Its distinctiveness from other innovation forms lies in its potential to facilitate the transformation of development modes, reshape economic structures, and address pollution prevention and control challenges. However, in the context of green innovation, based on the double-difference approach, Wang et al. (2020) has pointed out that national high-tech zones enhance the effectiveness of urban green innovation, but this is only significant in the eastern region.

Furthermore, scholars have also explored the mechanisms underlying the innovation effects of national high-tech. For example, Cattapan et al. (2012) focused on science parks in Italy. They found that green innovation represents a potent avenue for achieving economic benefits as the primary driver of sustainable development, and environmental value technology transfer services positively influence product innovation. Albahari et al. (2017) confirmed that higher education institutions’ involvement in advancing corporate innovation within technology and science parks has a beneficial moderating effect. Using the moderating effect of spatial agglomeration as a basis, Li WH. et al. (2022) found that industrial agglomeration has a significantly unfavorable moderating influence on the effectiveness of performance transformation in national high-tech zones. Multiple studies have examined the national high-tech zone industrial policy’s regulatory framework and urban innovation. However, in the age of rapidly expanding new infrastructure, infrastructure construction is concentrated on information technologies like blockchain, big data, cloud computing, artificial intelligence, and the Internet; Further research is needed to explore whether traditional infrastructure, particularly transportation infrastructure, can promote urban green innovation. Transportation infrastructure has consistently been vital in fostering economic expansion, integrating regional resources, and facilitating coordinated development ( Behrens et al., 2007 ; Zhang et al., 2018 ; Pokharel et al., 2021 ). Therefore, it is necessary to investigate whether transportation infrastructure can continue encouraging innovative urban green practices in the digital economy.

In summary, the existing literature has extensively examined the influence of national high-tech zones on economic growth and innovation from various levels and perspectives, establishing a solid foundation and offering valuable research insights for this study. Nonetheless, previous studies frequently overlooked the impact of national high-tech zones on urban green innovation levels, and a subsequent series of work in this paper aims to address this issue. Further exploration and expansion are needed to understand the industrial policy framework’s strategy for relating national high-tech zones to urban green innovation. Furthermore, there is a need for further improvement and refinement of the research model and methodology. Based on these, this paper aims to discuss the industrial policy effects of national high-tech zones from the perspective of urban green innovation to enrich and expand the existing research.

In contrast to earlier research, the marginal contribution of this paper is organized into three dimensions: 1) Most scholars have primarily focused on the effects of national high-tech zones on economic activity and innovation, with less emphasis on green innovation and rare studies according to the level of green innovation perspective. The study on national high-tech zones as an industrial policy that has already been done is enhanced by this work. 2) Regarding the research methodology, the Double Machine Learning (DML) approach is used to evaluate the policy effects of national high-tech zones, leveraging the advantages of machine learning algorithms for high-dimensional and non-parametric prediction. This approach circumvents the problems of model setting bias and the “curse of dimensionality” encountered in traditional econometric models ( Chernozhukov et al., 2018 ), enhancing the credibility of the research findings. 3) By introducing transportation infrastructure as a moderator variable, this study investigates the underlying mechanism of national high-tech zones on urban green innovation, offering suggestions for maximizing the influence of these zones on policy.

2.2 Theoretical analysis and hypotheses

2.2.1 national high-tech zones’ industrial policies and urban green innovation.

As one of the ways to land industrial policies at the national level, national high-tech zones serve as effective driving forces for enhancing China’s ability to innovate regionally and its contribution to economic growth ( Xu et al., 2022 ). Green innovation is a novel form of innovation activity that harmoniously balances the competing goals of environmental preservation and technological advancement, facilitating the superior expansion of the economy by alleviating the strain on resources and the environment ( Li, 2015 ). National high-tech zones mainly impact urban green innovation through three main aspects. Firstly, based on innovation compensation effects, national high-tech zones, established based on the government’s strategic planning, receive special treatment in areas such as land, taxation, financing, credit, and more, serving as pioneering special zones and experimental fields established by the government to promote high-quality regional development. When the government offers R&D subsidies to enterprises engaged in green innovation activities within the zones, enterprises are inclined to respond positively to the government’s policy support and enhance their level of green innovation as a means of seeking external legitimacy ( Fang et al., 2021 ), thereby contributing to the advancement of urban green innovation. Secondly, based on the industrial restructuring effect, strict regulation of businesses with high emissions, high energy consumption, and high pollution levels is another aspect of implementing the national high-tech zone program. Consequently, businesses with significant emissions and energy consumption are required to optimize their industrial structure to access various benefits within the park, resulting in the gradual transformation and upgrading of high-energy-consumption industries towards green practices, thereby further contributing to regional green innovation. Based on Porter’s hypothesis, the green and low-carbon requirements of the park policy increase the production costs for polluting industries, prompting polluting enterprises to upgrade their existing technology and adopt green innovation practices. Lastly, based on the theory of industrial agglomeration, the national high-tech zones’ industrial policy facilitates the concentration of innovative talents to a certain extent, resulting in intensified competition in the green innovation market. Increased competition fosters the sharing of knowledge, technology, and talent, stimulating a market environment where the survival of the fittest prevails ( Melitz and Ottaviano, 2008 ). These increase the effectiveness of urban green innovation, helping to propel urban green innovation forward. Furthermore, the infrastructure development within the national high-tech zones establishes a favorable physical environment for enterprises to engage in creative endeavors. Also, it enables the influx of high-quality innovation capital from foreign sources, complementing the inherent characteristics of national high-tech zones that attract such capital and concentrate green innovation resources, ultimately resulting in both environmental and economic benefits. Based on the above analysis, Hypothesis 1 is proposed:

Hypothesis 1. Implementing industrial policies in national high-tech zones enhances levels of urban green innovation.

2.2.2 Heterogeneity analysis

Given the variations in economic foundations, industrial statuses, and population distributions across different regions, development strategies in different regions are also influenced by these variations ( Chen and Zheng, 2008 ). Theoretically, when using administrative boundaries or geographic locations as benchmarks, the impact of national high-tech zone industrial policy on urban green innovation should be achieved through strategies like aligning with the region’s existing industrial structure. Compared to the western and central regions, the eastern region exhibits more incredible innovation and dynamism due to advantages such as a developed economy, good infrastructure, advanced management concepts, and technologies, combined with a relatively high initial level of green innovation factor endowment. Considering the diminishing marginal effect principle of green innovation, the industrial policy implementation in national high-tech zones favors an “icing on the cake” approach in the eastern region, contrasting with a “send carbon in the snow” approach in the central and western regions. In other words, the economic benefits of national high-tech zones for promoting urban green innovation may need to be more robust than their impact on the central and western regions. Literature confirms that establishing national high-tech zones yields a more beneficial technology agglomeration effect in the less developed central and western regions ( Liu and Zhao, 2015 ), leading to a more substantial impact on enhancing the level of urban green innovation.

Moreover, local governments consider economic development, industrial structure, and infrastructure levels when establishing national high-tech zones. These factors serve as the foundation for regional classification to address variations in regional quality and to compensate for gaps in theoretical research on the link between national high-tech zone industrial policy implementation and urban green innovation. Consequently, the execution of industrial policies in national high-tech zones relies on other vital factors influencing urban green innovation. Significant variations exist in economic development and infrastructure levels among cities of different grades ( Luo and Wang, 2023 ). Generally, cities with higher rankings exhibit strong economic growth and infrastructure, contrasting those with lower rankings. Consequently, the effect of establishing a national high-tech zone on green innovation may vary across different city grades. Thus, considering the disparities across city rankings, we delve deeper into identifying the underlying reasons for regional diversity in the green innovation outcomes of industrial policies implemented in national high-tech zones based on city grades. Based on the above analysis, Hypothesis 2 is proposed:

Hypothesis 2. There is regional heterogeneity and city-level heterogeneity in the impact of national high-tech zone policies on the level of urban green innovation.

2.2.3 The moderating effect of transportation infrastructure

Implementing industrial policies and facilitating the flow of innovation factors are closely intertwined with the role of transport infrastructure as carriers and linkages. Generally, enhanced transportation infrastructure facilitates the absorption of local factors and improves resource allocation efficiency, thereby influencing the spatial redistribution of production factors like labor, resources, and technology across cities. Enhanced transportation infrastructure fosters the development of more robust and advanced innovation networks ( Fritsch and Slavtchev, 2011 ). Banister and Berechman (2001) highlighted that transportation infrastructure exhibits network properties that are fundamental to its agglomeration or diffusion effects. From this perspective, robust infrastructure impacts various economic activities, including interregional labor mobility, factor agglomeration, and knowledge exchange among firms, thereby expediting the spillover effects of green technological innovations ( Yu et al., 2013 ). In turn, this could positively moderate the influence of national hi-tech zone policies on green innovation. On the other hand, while transportation infrastructure facilitates the growth of national high-tech zone policies, it also brings negative impacts, including high pollution, emissions, and ecological landscape fragmentation. Improving transportation infrastructure can also lead to the “relative congestion effect” in national high-tech zones. This phenomenon, observed in specific regions, refers to the excessive concentration of similar enterprises across different links of the same industrial chain, which exacerbates the competition for innovation resources among enterprises, making it challenging for enterprises in the region to allocate their limited innovation resources to technological research and development activities ( Li et al., 2015 ). As a result, there needs to be a higher green innovation level. Therefore, the impact of transportation infrastructure in the current stage of development will be more complex. When the level of transport infrastructure is moderate, adequate transport infrastructure supports the promotion of urban green innovation through national high-tech zone policies. However, the impact of transport infrastructure regulation may be harmful. Based on the above analysis, Hypothesis 3 is proposed:

Hypothesis 3. Transportation infrastructure moderates the relationship between national high-tech zones and levels of urban green invention.

3 Research design

3.1 model setting.

This research explores the impact of industrial policies of national high-tech zones on the level of urban green innovation. Many related studies utilize traditional causal inference models to assess the impact of these policies. However, these models have several limitations in their application. For instance, the commonly used double-difference model in the parallel trend test has stringent requirements for the sample data. Although the synthetic control approach can create a virtual control group that meets parallel trends’ needs, it is limited to addressing the ‘one-to-many’ problem and requires excluding groups with extreme values. The selection of matching variables in propensity score matching is subjective, among other limitations ( Zhang and Li, 2023 ). To address the limitations of conventional causal inference models, scholars have started to explore applying machine learning to infer causality ( Chernozhukov et al., 2018 ; Knittel and Stolper, 2021 ). Machine learning algorithms excel at an impartial assessment of the effect on the intended target variable for making accurate predictions.

In contrast to traditional machine learning algorithms, the formal proposal of DML was made in 2018 ( Chernozhukov et al., 2018 ). This approach offers a more robust approach to causal inference by mitigating bias through the incorporation of residual modeling. Currently, some scholars utilize DML to assess causality in economic phenomena. For instance, Hull and Grodecka-Messi (2022) examined the effects of local taxation, crime, education, and public services on migration using DML in the context of Swedish cities between 2010 and 2016. These existing research findings serve as valuable references for this study. Compared to traditional causal inference models, DML offers distinct advantages in variable selection and model estimation ( Zhang and Li, 2023 ). However, in promoting urban green innovation in China, there is a high probability of non-linear relationships between variables, and the traditional linear regression model may lead to bias and errors. Moreover, the double machine learning model can effectively avoid problems such as setting bias. Based on this, the present study employs a DML model to evaluate the policy implications of establishing a national high-tech zone.

3.1.1 Double machine learning framework

Prior to applying the DML algorithm, this paper refers to the practice of Chernozhukov et al. (2018) to construct a partially linear DML model, as depicted in Eq. 1 below:

where i represents the city, t represents the year, and l n G I i t represents the explained variable, which in this paper is the green innovation level of the city. Z o n e i t represents the disposition variable, which in this case is a national high-tech zone’s policy variable. It takes a value of 1 after the implementation of the pilot and 0 otherwise. θ 0 is the disposal factor that is the focus of this paper. X i t represents the set of high-dimensional control variables. Machine learning algorithms are utilized to estimate the specific form of g ^ X i t , whereas U i t , which has a conditional mean of 0, stands for the error term. n represents the sample size. Direct estimation of Eq. 1 provides an estimate for the coefficient of dispositions.

We can further explore the estimation bias by combining Eqs 1 , 2 as depicted in Eq. ( 3 ) below:

where a = 1 n ∑ i ∈ I , t ∈ T   Z o n e i t 2 − 1 1 n ∑ i ∈ I , t ∈ T   Z o n e i t U i t , by a normal distribution having 0 as the mean, b = 1 n ∑ i ∈ I , t ∈ T   Z o n e i t 2 − 1 1 n ∑ i ∈ I , t ∈ T   Z o n e i t g X i t − g ^ X i t . It is important to note that DML utilizes machine learning and a regularization algorithm to estimate a specific functional form g ^ X i t . The introduction of “canonical bias” is inevitable as it prevents the estimates from having excessive variance while maintaining their unbiasedness. Specifically, the convergence of g ^ X i t to g X i t , n −φg > n −1/2 , as n tends to infinity, b also tends to infinity, θ ^ 0 is difficult to converge to θ 0 . To expedite convergence and ensure unbiasedness of the disposal coefficient estimates with small samples, an auxiliary regression is constructed as follows:

where m X i t represents the disposition variable’s regression function on the high-dimensional control variable, this function also requires estimation using a machine learning algorithm in the specific form of m ^ X i t . Additionally, V i t represents the error term with a 0 conditional mean.

3.1.2 The test of the mediating effect within the DML framework

This study investigates how the national high-tech zone industrial policy influences the urban green innovation. It incorporates moderating variables within the DML framework, drawing on the testing procedure outlined by Jiang (2022) , and integrates it with the practice of He et al. (2022) , as outlined below:

Equation 5 is based on Eq. 1 with the addition of variables l n t r a i t and Z o n e i t * l n t r a i t .Where l n t r a i t represents the moderating variable, which in this paper is the transportation infrastructure. Z o n e i t * l n t r a i t represents the interaction term of the moderating variable and the disposition variable. The variables l n t r a i t and Z o n e i t are added to the high-dimensional control variables X i t , and the rest of the variables in Eq. 5 are identical to Eq. 1 . θ 1 represents the disposal factor to focus on.

3.2 Variable selection

3.2.1 dependent variable: level of urban green innovation (lngi).

Nowadays, many academics use indicators like the number of applications for patents or authorizations to assess the degree of urban innovation. To be more precise, the quantity of patent applications is a measure of technological innovation effort, while the number of patents authorized undergoes strict auditing and can provide a more direct reflection of the achievements and capacity of scientific and technological innovation. Thus, this paper refers to the studies of Zhou and Shen (2020) and Li X. et al. (2022) to utilize the count of authorized green invention patents in each prefecture-level city to indicate the level of green innovation. For the empirical study, the count of authorized green patents plus 1 is transformed using logarithm.

3.2.2 Disposal variable: dummy variables for national high-tech zones (Zone)

The national high-tech zone dummy variable’s value correlates with the city in which it is located and the list of national high-tech zones released by China’s Ministry of Science and Technology. If a national high-tech zone was established in the city by 2017, the value is set to 1 for the year the high-tech zone is established and subsequent years. Otherwise, it is set to 0.

3.2.3 Moderating variable: transportation infrastructure (lntra)

Previous studies have shown that China’s highway freight transport comprises 75% of the total freight transport ( Li and Tang, 2015 ). Highway transportation infrastructure has a significant influence on the evolution of the Chinese economy. The development and improvement of highway infrastructure are crucial for modern transportation. This paper uses the research methods of Wu (2019) and uses the roadway mileage (measured in kilometers) to population as a measure of the quality of the transportation system.

3.2.4 Control variables

(1) Foreign direct investment (lnfdi): There is general agreement among academics that foreign direct investment (FDI) significantly influences urban green innovation, as FDI provides expertise in management, human resources, and cutting-edge industrial technology ( Luo et al., 2021 ). Thus, it is necessary to consider and control the level of FDI. This paper uses the ratio of foreign investment to the local GDP in a million yuan.

(2) Financial development level (lnfd): Innovation in science and technology is greatly aided by finance. For the green innovation-driven strategy to advance, it is imperative that funding for science and technology innovation be strengthened. The amount of capital raised for innovation is strongly impacted by the state of urban financial development ( Zhou and Du, 2021 ). Thus, this paper uses the loan balance to GDP ratio as an indicator.

(3) Human capital (lnhum): Highly skilled human capital is essential for cities to drive green innovation. Generally, highly qualified human capital significantly boosts green innovation ( Ansaris et al., 2016 ). Therefore, a measure was employed: the proportion of people in the city who had completed their bachelor’s degree or above.

(4) Industrial structure (lnind): Generally, the secondary industry in China is the primary source of pollution, and there is a significant impact of industrial structure on green innovation ( Qiu et al., 2023 ). The metric used in this paper is the secondary industry-to-GDP ratio for the area.

(5) Regional economic development level (lnagdp): A region’s level of economic growth is indicative of the material foundation for urban green innovation and in-fluences the growth of green innovation in the region ( Bo et al., 2020 ). This research uses the annual gross domestic product per capita as a measurement.

3.3 Data source

By 2017, China had developed 157 national high-tech zones in total. In conjunction with the study’s objectives, this study performs sample adjustments and a screening process. The study’s sample period spans from 2007 to 2019. 57 national high-tech zones that were created prior to 2000 are omitted to lessen the impact on the test results of towns having high-tech zones founded before 2007. Due to the limitations of high-tech areas in cities at the county level in promoting urban green innovation, 8 high-tech zones located in county-level cities are excluded. And 4 high-tech zones with missing severe data are excluded. Among the list of established national high-tech zones, 88 high-tech zones are distributed across 83 prefecture-level cities due to multiple districts within a single city. As a result, 83 cities are selected as the experimental group for this study. Additionally, a control group of 80 cities was selected from among those that did not have high-tech zones by the end of 2019, resulting in a final sample size of 163 cities. This paper collects green patent data for each city from the China Green Patent Statistical Report published by the State Intellectual Property Office. The author compiled the list of national high-tech zones and the starting year of their establishment on the official government website. In addition, the remaining data in this paper primarily originated from the China Urban Statistical Yearbook (2007–2019), the EPS database, and the official websites of the respective city’s Bureau of Statistics. Missing values were addressed through linear interpolation. To address heteroskedasticity in the model, the study logarithmically transforms the variables, excluding the disposal variable. Table 1 shows the descriptive analysis of the variables.

Table 1 . Descriptive analysis.

4 Empirical analysis

4.1 national high-tech zones’ policy effects on urban green innovation.

This study utilizes the DML model to estimate the impact of industrial policies implemented in national high-tech zones at the level of urban green innovation. Following the approach of Zhang and Li (2023) , the sample is split in a ratio of 1:4, and the random forest algorithm is used to perform predictions and combine Eq. ( 1 ) with Eq. ( 4 ) for the regression. Table 2 presents the results with and without controlling for time and city effects. The results indicate that the treatment effect sizes for these four columns are 0.376, 0.293, 0.396, and 0.268, correspondingly, each of which was significant at a 1% level. Thus, Hypothesis 1 is supported.

Table 2 . Benchmark regression results.

4.2 Robustness tests

4.2.1 eliminate the influence of extreme values.

To reduce the impact of extreme values on the estimation outcomes, all variables on the benchmark regression, excluding the disposal variable, undergo a shrinkage process based on the upper and lower 1% and 5% quantiles. Values lower than the lowest and higher than the highest quantile are replaced accordingly. Regression analyses are conducted. Table 3 demonstrates that removing outliers did not substantially alter the findings of this study.

Table 3 . Extreme values removal results.

4.2.2 Considering province-time interaction fixed effects

Since provinces are critical administrative units in the governance system of the Chinese government, cities within the same province often share similarities in policy environment and location characteristics. Therefore, to account for the influence of temporal changes across different provinces, this study incorporates province-time interaction fixed effects based on the benchmark regression. Table 4 presents the individual regression results. Based on the regression results, after accounting for the correlation between different city characteristics within the same province, national high-tech zone policies continue to significantly influence urban green innovation, even at the 1% level.

Table 4 . The addition of province and time fixed effects interaction terms.

4.2.3 Excluding other policy disturbances

When analyzing how national high-tech zones affect strategy for urban green innovation, it is susceptible to the influence of concurrent policies. This study accounts for other comparable policies during the same period to ensure an accurate estimation of the policy effect. Since 2007, national high-tech zone policies have been successively implemented, including the development of “smart cities.” Therefore, this study incorporates a policy dummy variable for “smart cities” in the benchmark regression. The specific regression findings are shown in Table 5 . After controlling for the impact of concurrent policies, the importance of national high-tech zones’ policy impact remains consistent.

Table 5 . Results of removing the impact of parallel policies.

4.2.4 Resetting the DML model

To mitigate the potential bias introduced by the settings in the DML model on the conclusions, the purpose of this study is to assess the conclusions’ robustness using the following methods. First, the sample split ratio of the DML model is adjusted from 1:4 to 1:2 to examine the potential impact of the sample split ratio on the conclusions of this study. Second, the machine learning algorithm is substituted, replacing the random forest algorithm, which has been utilized as a prediction algorithm, with lasso regression, gradient boosting, and neural networks to investigate the potential influence of prediction algorithms on the conclusions of this study. Third, regarding benchmark regression, additional linear models were constructed and analyzed using DML, which involves subjective decisions regarding model form selection. Therefore, DML was employed to construct more comprehensive interactive models, aiming to assess the influence of model settings on the conclusions of this study. The main and auxiliary regressions utilized for the analysis were modified as follows:

Combining Eqs ( 7 ), ( 8 ) for the regression, the interactive model yielded estimated coefficients for the disposition effect:

The results of Eq. ( 9 ) are shown in column (5) of Table 6 . And all the regression results obtained from the modified DML model are presented in Table 6 .

Table 6 . Results of resetting the DML model.

The findings indicate that the sample split ratio in the DML model, the prediction algorithm used, or the model estimation approach does not impact the conclusion that the national high-tech zone policy raises urban areas’ level of green innovation. These factors only modify the magnitude of the policy effect to some degree.

4.3 Heterogeneity analysis

4.3.1 regional heterogeneity.

The sample cities were further divided into the east, central, and west regions based on the three major economic subregions to examine regional variations in national high-tech zone policies ' effects on urban green innovation, with the results presented in Table 7 . National high-tech zone policies do not statistically significantly affect urban green innovation in the eastern region. However, they have a considerable beneficial influence in the central and western areas. The lack of statistical significance may be explained by the possibility that the setting up of national high-tech zones in the eastern region will provide obstacles to the growth of urban green innovation, such as resource strain and environmental pollution. Given the central and western regions’ relatively underdeveloped economic status and industrial structure, coupled with the preceding theoretical analysis, establishing national high-tech zones is a crucial catalyst, significantly boosting urban green innovation levels. Furthermore, the central government emphasizes that setting high-tech national zones should consider regional resource endowments and local conditions, implementing tailored policies. The central and western regions possess unique geographic locations and natural conditions that make them well-suited for developing solar energy, wind energy, and other forms of green energy. Compared to the central region, the national high-tech zone initiative has a more pronounced impact on promoting urban green innovation in the western region. While further optimization is needed for the western region’s urban innovation environment, the policy on national high-tech zones has a more substantial incentive effect in this region due to its more significant development potential, positive transformation of industrial structure, and increased policy support from the state, including the development strategy for the western region.

Table 7 . Heterogeneity test results for different regions.

4.3.2 Urban hierarchical heterogeneity

The New Tier 1 Cities Institute’s ‘2020 City Business Charm Ranking’ is the basis for this study, with the sample cities categorized into Tier 1 (New Tier 1), Tier 2, Tier 3, Tier 4, and Tier 5. Table 8 presents the regression findings for each of the groups.

Table 8 . Heterogeneity test results for different classes of cities.

The results in Table 8 reveal significant heterogeneity at the city level regarding national high-tech zones’ effects on urban green innovation, confirming Hypothesis 2 . In particular, the coefficients for the first-tier cities are not statistically significant due to the small sample size, and the same applies to the fifth-tier cities. This could be attributed to the relatively weak economy and infrastructure development issues in the fifth-tier cities. Additionally, due to their limited level of development, the fifth-tier cities may have a relatively homogeneous industrial structure, with a dominance of traditional industries or agriculture and a need for a more diversified industrial layout. National high-tech zones have not greatly aided the development of green innovation in these cities. In contrast, national high-tech zone policies in second-tier, third-tier, and fourth-tier cities have a noteworthy favorable impact on green innovation, indicating their favorable influence on enhancing green innovation in these cities. Despite the lower level of economic development in fourth-tier cities compared to second-tier and third-tier cities, the fourth-tier cities’ national high-tech zones have the most pronounced impact on promoting green innovation. This could be attributed to the ongoing transformation of industries in fourth-tier cities, which are still in the technology diffusion and imitation stage, allowing these cities’ national high-tech zones to maintain a high marginal effect. Thus, Hypothesis 2 is supported.

5 Further analysis

According to the empirical findings, setting high-tech national zones significantly raises the bar for urban green innovation. Therefore, it is essential to understand the underlying factors and mechanisms that contribute to the positive correlation. This paper constructs a moderating effect test model using Eqs 5 , 6 and provides a detailed discussion by introducing transportation infrastructure as a moderating variable.

The empirical finding of the moderating impact of transportation infrastructure is shown in Table 9 . The dichotomous interaction term Zone*lntra is significantly negative at the 5% level, suggesting that the impact of national high-tech zone policies on the level of urban green innovation is negatively moderated by transportation infrastructure. This result deviates from the general expectation, but it aligns with the complexity of the role played by transportation infrastructure in the context of modern economic development, as discussed in the previous theoretical analysis. This could be attributed to the insufficient green innovation benefits generated by the policy on national high-tech zones at the current stage, which fails to compensate for the adverse effects of excessive resource consumption and environmental pollution caused by the construction of the zone. Furthermore, transportation infrastructure can lead to an excessive concentration of similar enterprises in the high-tech zones. This excessive concentration creates a relative crowding effect, intensifying competition among enterprises. It diminishes their inclination to engage in green innovation collaboration and investment and hinders their effective implementation of technological research and development activities. Moreover, the excessive clustering of similar enterprises implies a need for more diversity in green innovation activities among businesses located in national high-tech zones. This results in duplicated green innovation outputs and hinders the advancement of green innovation. Thus, Hypothesis 3 is supported.

Table 9 . Empirical results of moderating effects.

6 Conclusion and policy recommendations

6.1 conclusion.

Based on panel data from 163 prefecture-level cities in China from 2007 to 2019, the net effect of setting national high-tech zones on urban green innovation was analyzed using the double machine learning model. The results found that: firstly, the national high-tech zone policy significantly raises the degree of local green innovation, and these results remain robust even after accounting for various factors that could affect the estimation results. Secondly, in the central and western regions, the level of urban green innovation is positively impacted by the national high-tech zone policy; However, this impact is less significant in the eastern region. In the western region compared to the central region, the national high-tech zone initiative has a stronger impact on increasing the level of urban green innovation. Across different city levels, compared to second-tier and third-tier cities, the high-tech zone policy has a more substantial impact on increasing the level of green innovation in fourth-tier cities. Thirdly, based on the moderating effect mechanism test, the construction of transportation infrastructure weakens the promotional effect of national high-tech zones on urban green innovation.

6.2 Policy recommendations

In order that national high-tech zones can better promote China’s high-quality development, this paper proposes the following policy recommendations:

(1) Urban green innovation in China depends on accelerating the setting up of national high-tech zones and creating an atmosphere that supports innovation. Establishing national high-tech zones as testbeds for high-quality development and green innovation has significantly elevated urban green innovation. Thus, cities can efficiently foster urban green innovation by supporting the development of national high-tech zones. Cities that have already established national high-tech zones should further encourage enterprises within these zones to increase their investment in research and development. They should also proceed to foster the leadership of national high-tech zones for urban green innovation, assuming the role of pilot cities as models and leaders. Additionally, it is essential to establish mechanisms for cooperation and synergy between the pilot cities and their neighboring cities to promote collective green development in the region.

(2) Expanding the pilot program and implementing tailored policies based on local conditions are essential. Industrial policies about national high-tech zones have differing effects on urban green innovation. Regions should leverage their comparative advantages, consider urban development’s commonalities and unique aspects, and foster a stable and sustainable green innovation ecosystem. The western and central regions should prioritize constructing and enhancing new infrastructure and bolster support for the high-tech green industry. The western region should seize the opportunity presented by national policies that prioritize support, quicken the rate of environmental innovation, and progressively bridge the gap with the eastern and central regions in various aspects. Furthermore, second-tier, third-tier, and fourth-tier cities should enhance the advantages of national high-tech zone policies, further maintaining the high standard of green innovation and keeping green innovation at an elevated level. Regions facing challenges in green innovation, particularly fifth-tier cities, should learn from the development experiences of advanced regions with national high-tech zones to compensate for their deficiencies in green innovation.

(3) Highlighting the importance of transportation regulation and enhancing collaboration in green innovation is crucial. Firstly, transportation infrastructure should be maximized to strengthen coordination and cooperation among regions, facilitate the smooth movement of innovative talents across regions, and facilitate the rational sharing of innovative resources, collectively enhancing green innovation. Additionally, attention ought to be given to the industrial clustering effect of parks to prevent the wastage of resources and inefficiencies resulting from the excessive clustering of similar industries. Efforts should be focused on effectively harnessing the latent potential of crucial transportation infrastructure areas as long-term drivers of development, promptly mitigating the negative impact of transportation infrastructure construction, and gradually achieving the synergistic promotion of the setting up of national high-tech zones and the raising of urban levels of green innovation, among other overarching objectives.

6.3 Limitations and future research

Our study has some limitations because the research in this paper is conducted in the institutional context of China. For example, not all countries are suitable for implementing similar industrial policies to develop the economy while focusing on environmental protection. However, we recognize that this study is interesting and relevant, and it encourages us to focus more intensely on environmental protection from an industrial policy perspective. Moreover, this paper exhibits certain limitations in the research process. Firstly, the urban green innovation measurement index was developed using the quantity of green patent authorizations. Future studies could focus on green innovation processes, such as the quality of green patents granted. Secondly, the paper employs machine learning techniques for causal inference. Subsequent investigations could delve further into the potential applications of machine learning algorithms in environmental sciences to maximize the benefits of innovative research methodologies.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

WC: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing–review and editing. YJ: Conceptualization, Data curation, Formal Analysis, Investigation, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–original draft, Writing–review and editing. BT: Investigation, Project administration, Writing–review and editing.

The authors declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Youth Fund for Humanities and Social Science research of Ministry of Education (20YJC790004).

Acknowledgments

The authors are grateful to the editors and the reviewers for their insightful comments.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: national high-tech zone, industrial policy, green innovation, heterogeneity analysis, moderating effect, double machine learning

Citation: Cao W, Jia Y and Tan B (2024) Impact of industrial policy on urban green innovation: empirical evidence of China’s national high-tech zones based on double machine learning. Front. Environ. Sci. 12:1369433. doi: 10.3389/fenvs.2024.1369433

Received: 12 January 2024; Accepted: 15 March 2024; Published: 04 April 2024.

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*Correspondence: Yu Jia, [email protected]