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Article

Driving Paths and Evolution Trends of Urban Low-Carbon Transformation: Configuration Analysis Based on Three Batches of Low-Carbon Pilot Cities

by
You-Dong Li
1 and
Chen-Li Yan
2,*
1
School of Business Administration, Inner Mongolia University of Finance and Economics, Hohhot 010070, China
2
School of Business Administration, Capital University of Economics and Business, Beijing 100070, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(17), 7630; https://doi.org/10.3390/su16177630
Submission received: 19 July 2024 / Revised: 29 August 2024 / Accepted: 30 August 2024 / Published: 3 September 2024
(This article belongs to the Section Environmental Sustainability and Applications)
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Abstract

:
In response to global climate challenges, urban low-carbon transformation has become a critical strategy for sustainable development. This study constructs a theoretical model for urban low-carbon transformation using the multi-level perspective framework. We focused on three batches of low-carbon pilot cities in China and employed fuzzy set qualitative comparative analysis to investigate the transformation pathways and impact mechanisms during the periods 2010–2012, 2012–2017, and 2017–2019. The results indicate that none of the six antecedent conditions is necessary for urban low-carbon transformation. Initially, the transformation is primarily driven by a pathway led by low-carbon industries. In the mid-stage, two pathways emerge: one dominated by the combination of low-carbon industries and research and development (R&D) human capital and another led by low-carbon consumption awareness and economic development levels. In the later stage, the influencing factors involve a combination across micro, meso, and macro levels, reflecting an increasingly diversified and intricate configuration. The regional industrial structure consistently plays a dominant role, while awareness of low-carbon consumption has grown over time. This study not only enhances our understanding of the underlying mechanisms but also provides practical policy recommendations for local governments to tailor their strategies for effective low-carbon transformation.

1. Introduction

The rapid increase in carbon dioxide emissions has significantly contributed to global climate change, leading to challenges such as global warming, energy shortages, and extreme weather events [1]. These challenges pose substantial threats to human health and hinder the sustainable development of economies worldwide [2]. As centers of economy, culture, and politics, cities play a key role in the critical strategy of low-carbon transformation to address global climate change [3]. Although cities occupy only 2–3% of the Earth’s surface [4], they generate 70% of carbon emissions [5], making urban low-carbon transformation essential to achieving global sustainability goals.
Current research on urban low-carbon transformation has predominantly focused on examining the influence of specific factors such as green economic growth [6], industrial structural adjustment [7], pilot policy implementation [8], and energy efficiency improvements [9]. Scholars have analyzed how green economic strategies can drive low-carbon growth. For instance, research on industrial structural adjustment has underscored the importance of transitioning from traditional, high-energy industries toward service-oriented and technology-driven sectors, significantly reducing urban carbon emissions [10]. Simultaneously, researchers have explored pilot policy initiatives, such as low-carbon pilot cities and regional carbon trading schemes, for their effectiveness in fostering urban low-carbon transformation [11]. Additionally, technological innovation, urban planning, and green infrastructure were found as critical drivers of low-carbon transformation [12].
Despite these advances, most studies have focused only on individual factors and overlooked the combined effects that shape urban low-carbon pathways. Urban systems are complex and dynamic, requiring nuanced understanding of how various interactions influence low-carbon strategies [13]. Addressing these challenges holistically is essential for developing effective, context-specific pathways to urban low-carbon transformation.
In this study, we constructed an urban low-carbon transformation model using the multi-level perspective (MLP) framework. This framework includes macro-level, meso-level, and micro-level components. Then, we applied fuzzy set qualitative comparative analysis (fsQCA) to explore urban low-carbon transformation pathways and impact mechanisms across different time periods. Our case studies focused on cities in three phases, marked by the announcement dates of China’s three batches of low-carbon pilot cities: 2010–2012, 2012–2017, and 2017–2019. Additionally, we compared the configurations of low-carbon transformation in these three batches of cities, observed how main factors change over time, and herein propose targeted suggestions.
This study offers three main contributions. First, this study emphasizes a holistic approach, demonstrating that various combinations of factors can achieve multiple urban low-carbon transformation pathways. Second, we applied the MLP framework to explore the synergistic effects of macro-, meso-, and micro-level factors, broadening its application in urban low-carbon research and providing a reference framework for future studies. Finally, we employed dynamic QCA methods to analyze multi-period urban low-carbon transformation pathways, revealing evolving patterns and factor changes. This study enhances the understanding of urban low-carbon transformation and aids in selecting tailored strategies suited to regional characteristics, ultimately promoting coordinated economic growth and carbon reduction.

2. Literature Review and Model Building

2.1. Literature Review

Low-carbon development has garnered considerable scholarly attention. Khanna et al. [14] defined low-carbon cities as an emerging paradigm characterized by minimal energy consumption and emissions, which are integral to sustainable development. Wang et al. [15] emphasized technological innovation, industrial upgrading, and energy transformation as key drivers for achieving low-carbon growth. Building on these principles, scholars have proposed development models centered around energy transition [16] as well as pilot policies [17] forming the theoretical and practical foundation for urban low-carbon transformation.
The research on factors influencing low-carbon transformation spans multiple dimensions. At the micro level, green low-carbon technologies play a pivotal role in reducing carbon emissions, particularly in sectors such as transportation where bio-fuels effectively decrease carbon intensity [18,19]. Scholars have extensively explored the relationship between green technology innovation and urban low-carbon transformation [20,21]. For example, Wu et al. [20] employed a difference-in-differences model to analyze this relationship and discovered that green technology innovation serves as a major driving force behind urban low-carbon transformation. This finding is supported by research on the role of technological advancements in promoting low-carbon patent applications and fostering innovation systems [21].
At the meso level, the regional industrial structure [22] and green innovation capabilities [23] are crucial determinants of urban low-carbon transformation. Regions with advanced industrial structures, characterized by high-tech and environmentally friendly sectors, are better positioned to effectively implement low-carbon strategies [24]. An optimized industrial structure not only facilitates carbon emission reductions but also supports sustainable economic growth through enhanced industrial upgrading and resource efficiency [25]. Additionally, green innovation plays a crucial role in driving urban low-carbon transformation. Studies have demonstrated that the combination of green technology innovation and industrial structure upgrading can significantly reduce carbon emissions, particularly in regions with strong innovation ecosystems [22]. However, the effectiveness of these factors varies according to regional characteristics like economic development stage, policy support, and city scale, underscoring the need for tailored approaches to urban low-carbon transformation strategies [23].
At the macro level, the socio-economic and policy environment plays a fundamental role in guiding urban low-carbon transformation. Research suggests that low-carbon initiatives not only mitigate climate change but also foster integrated growth across these dimensions [26]. Moreover, effective coordination of macro-level policies such as national energy transition strategies and international climate agreements is vital in shaping urban low-carbon pathways [27]. Furthermore, regional economic development significantly enhances capacity for green growth and sustainable practices as evidenced by studies on urban low-carbon performance [28]. Public awareness and engagement equally play an important role in encouraging adoption of low-carbon behaviors and promoting sustainable practices [29].
In conclusion, current research on urban low-carbon transformation often focuses on theoretical perspectives or single-variable empirical studies. However, this is a complex social governance issue involving the interplay of many factors. A single-variable approach cannot capture the full complexity of urban low-carbon transformation. Therefore, further exploration of the intricate mechanisms and dynamic pathways is needed.

2.2. MLP Framework

The MLP framework examines socio-technical systems holistically, considering factors like technology, environment, institutions, and culture. It drives systemic innovation through the co-evolution of three levels: the socio-technical landscape (macro level), regimes (meso level), and niches (micro level) [30]. The framework emphasizes dynamic interactions across these levels, fostering system-wide collaboration and evolution [31]. The MLP focuses on interactions among actors in innovation diffusion rather than technological innovation alone, which is widely applied in sustainable development and energy transitions. Technological advances and user environments have co-adapted and lead to mutual development of technology, environment, and society [31].
According to Geels, the stability of socio-technical systems is maintained through interactions among material elements, actors, networks, and guiding rules [32]. Raven et al. argued that low-carbon transformation requires not only technological diffusion but also shifts in user practices, market environments, and broader policies [33]. These transitions emerge from interactions among technologies, markets, policies, and behaviors, creating a comprehensive system of socio-technical change [34]. Our research explores factors influencing urban low-carbon transformation across macro, meso, and micro levels, extending the MLP model’s application and providing theoretical and practical insights for urban low-carbon transformation.

2.3. Urban Low-Carbon Transformation Framework

The urban low-carbon transformation framework integrates influences at the micro, meso, and macro levels, each contributing essential components for a successful low-carbon transformation.
Micro Level (Niche): The micro level focuses on foundational elements driving innovation in low-carbon technologies [35], including R&D investment intensity and human capital. High investment in research and development, along with a skilled workforce, provides the resources and expertise needed for breakthrough innovations [36]. These niche innovations are critical, serving as seeds for technological advancements that contribute to broader low-carbon transformation initiatives [37].
Meso Level (System Layer): The meso level involves the regional industrial structure and the level of green innovation. Optimizing the industrial structure plays a key role in reducing carbon emissions by enhancing energy efficiency, promoting clean energy use, and reducing reliance on high-carbon industries [38]. Concurrently, advancing green innovation ensures technological progress aligns with environmental goals, supporting sustainable economic growth and facilitating urban low-carbon transformation [39].
Macro Level (Landscape): The macro level sets the broader context, including low-carbon consumption awareness and the economic development level. Public awareness and adoption of low-carbon behaviors drive demand for sustainable products and services, which incentivizes continued investment in green technologies [40]. Additionally, a region’s economic development level affects its ability to invest in low-carbon infrastructure and innovation. Regions with strong economic foundations are better equipped to support large-scale transformations through financial resources and policy initiatives [41].
Interaction Mechanism Across Levels: Increased R&D investment and human capital at the micro level rapidly drive technological innovation, particularly radical innovations, which in turn boost green innovation and optimize industrial structures [36]. At the meso level, improving industrial structure and green innovation reduces costs for developing micro-level technologies, creating a supportive environment for low-carbon transformation [37]. Strong regional green innovation not only supports micro-level advancements but also attracts talent, lowering development costs further [42]. At the macro level, financial support for R&D fosters niche innovations, while growing awareness of low-carbon practices drives investment across the entire regional ecosystem, reinforcing the interactions among all levels [43].
These three levels are interconnected, dynamically influencing the overall trajectory of urban low-carbon transformation. The interplay between niche innovations, system-level adjustments, and macro-level factors creates a comprehensive approach to achieving low-carbon goals. Analyzing these factors across different periods helps clarify the strategies and conditions necessary for successful urban low-carbon transformation (as shown in Figure 1).

3. Methods and Data

3.1. FsQCA Method

The QCA method, introduced by Ragin in 1987, is outcome-oriented. It focuses on understanding configurational effects, making it ideal for addressing complex causal relationships [44]. We applied fsQCA for two main reasons: First, as a case-oriented approach based on set theory and Boolean algebra, fsQCA identifies configurations linked to specific outcomes. It offers insights into complementarity, configurational equifinality, and causal asymmetry among factors [45]. Second, fsQCA handles continuous data and better captures subtle variations in factors across different levels. It is suitable for analyzing the complex dynamics in urban low-carbon transformation [46].
The fsQCA process begins by selecting condition and outcome variables based on theory and empirical evidence [44]. Next, we calibrate the data to transform raw data into fuzzy set scores (ranging from 0 to 1) with thresholds for full membership, non-membership, and a crossover point. We then construct a truth table that lists all possible configurations of conditions and their association with the outcome [47]. The analysis identifies necessary and sufficient conditions and highlights key causal configurations [46]. Finally, we perform robustness checks to ensure the stability and reliability of the results [45]. By applying fsQCA, this study not only reveals the multi-dimensional interactions among various factors but also offers practical pathways for urban low-carbon transformation.

3.2. Sample Selection and Data Source

This study focuses on China’s low-carbon pilot cities, categorized into three periods based on announcement dates in 2010, 2012, and 2017. Cities at or above the prefectural level were selected, with Lhasa excluded due to data gaps. The three periods—2010 to 2012, 2012 to 2017, and 2017 to 2019—enable a structured analysis of policy effects, involving 13 cities in the first period, 24 additional cities in the second, and 33 more in the third, as outlined in Table 1 and Figure 2. To account for the lag in policy implementation, data from the final year of each stage (2011, 2016, and 2019) were employed to analyze the combinations of various factors in urban low-carbon transformation. The primary data sources were the “China Statistical Yearbook”, “China Urban Statistical Yearbook”, and “China Energy Statistical Yearbook”, published in 2012, 2017, and 2020.

3.3. Measurement and Calibration

3.3.1. Outcome Measurement (Urban Low-Carbon Transformation)

Urban carbon intensity: We used urban carbon intensity to measure the outcome, calculated as the ratio of urban carbon emissions to the actual regional gross domestic product (GDP).
Our calculations for urban carbon emissions incorporated various sources, including emissions from direct energy consumption such as natural gas, liquefied petroleum gas, and coal gas, along with emissions from electricity and heat consumption. For natural gas, liquefied petroleum gas, and coal gas, we applied conversion factors provided by the IPCC. Carbon emissions from electricity consumption were determined using the baseline emission factors specific to each regional power grid in China, which is divided into six major regions: northern China, the northeast, eastern China, central China, the northwest, and the south [48]. Heat energy emissions were primarily calculated using the standard coal conversion coefficient of 0.7143 kg of standard coal per kilogram [49].

3.3.2. Conditioned Measurements

R&D investment intensity plays a pivotal role in driving technological advancements and product innovation, thereby expediting the development of low-carbon solutions in urban areas. This intensity was quantified as the ratio of (R&D expenditures + education expenditures) to regional GDP [50]. R&D human capital was measured using full-time equivalents for R&D personnel [51]. Regional industrial structure was measured using the rate of change in the ratio of secondary industry value added to regional GDP [52]. Green technology innovation was measured using the number of green patents granted to listed companies in each city [53].
Economic development level was measured by per capital GDP, with cities having higher per capital GDP often exhibiting better economic development [54]. Therefore, we used per capital GDP as a measure of economic development level. Low-carbon consumption awareness was measured using the ratio of public electric vehicles to the total number of public electric vehicles and public taxis at the end of the year [55]. Green transportation options can directly reduce gasoline consumption and carbon emissions [56]. Since the 2019 dataset encompasses all case samples (a total of 70), data from 2019 were used for the descriptive statistics. The specific variable measurement standards and data sources are detailed in Table 2, while the descriptive statistics results are presented in Table 3.

3.3.3. Data Calibration

Data calibration in fsQCA is essential for converting raw data into consistent membership scores [44]. These scores range from 0 (non-membership) to 1 (full membership), with a crossover point typically set at 0.5 to indicate maximum ambiguity [47]. Following existing research [55,56], we set the critical values for most condition variables at the 95th, 50th, and 5th percentiles to align variable distributions with these anchor points. Urban low-carbon transformation is measured by carbon intensity, where lower values indicate better transformation. Therefore, reverse calibration was applied, setting the critical values at the 5th, 50th, and 95th percentiles [56]. The regional industrial structure was measured by the share of the secondary industry, which correlates with higher carbon emissions. Consequently, reverse calibration was used, with critical values set at the 10th, 50th, and 90th percentiles due to the large standard deviation relative to the mean [56]. Specific calibration data for each condition and outcome are detailed in Table 4.

4. Results

4.1. The Role of Individual Conditions in Urban Low-Carbon Transformation

Before conducting configurational analysis, it was necessary to test whether individual conditions are necessary for urban low-carbon transformation [45]. The necessity was determined by a consistency threshold, set at 0.9 following established research standards [45,47]. Using fsQCA 3.0 software, we examined whether conditions are necessary for urban low-carbon transformation. The analysis results are presented in Table 5. Among the six conditions, the consistency levels for their impact on high-level urban low-carbon transformation across the three time periods were all below 0.9, indicating they do not constitute necessary conditions. This finding underscores the complexity of urban low-carbon transformation and highlights the need to explore the synergistic effects of multiple conditions on the outcome.

4.2. Multiple Paths of Urban Low-Carbon Transformation

Based on the principle that the consistency threshold should be no less than 0.75, and the frequency threshold should be 1 for small to medium samples [47,56,57], we set the consistency thresholds at 0.8, 0.94, and 0.96. The frequency threshold was fixed at 1. These adjustments ensured the most meaningful configurations were identified. They also captured the unique dynamics of low-carbon transformation at each stage. We used fsQCA 3.0 software to conduct a sufficiency analysis on the six antecedent conditions. This analysis determined the core conditions and marginal conditions for each configuration [58]. The analysis results are shown in Table 6 and Table 7.

4.2.1. The First Stage Paths (2010–2012)

In the first stage, the overall consistency and sub-path consistency of the configurations were above the acceptable threshold of 0.75 (as shown in Table 6), resulting in three distinct pathways (as shown in Table 8). Configuration 1a shows that reducing the share of the secondary industry, combined with increased green innovation, promotes low-carbon transformation. Configuration 1b suggests that a significant reduction in the secondary industry share, combined with increased R&D investment, drives transformation. Configuration 1c indicates that, when the regional industrial structure plays a dominant role, enhancements in R&D human capital, low-carbon consumption awareness, and economic development lead to successful transformation. All three configurations center on reducing the secondary industry share, leading to the “low-carbon industry-driven” pathway. Transaction cost theory can explain this phenomenon, as cities initially prioritize strategies with lower implementation costs. At this stage, low-carbon consumption awareness was weak, and heavy industry was dominant. Therefore, reducing the share of the secondary industry is a practical approach in the early stages. For example, Guangzhou, one of China’s economically developed cities, focuses on reducing high-carbon industries and promoting the service and high-tech sectors during 2010–2012. In 2010, the Guangzhou Municipal Government issued the “Guiding Opinions on Vigorously Developing a Low-Carbon Economy”, highlighting low-carbon industry development, resource conservation, energy optimization, and environmental protection as key priorities.
Substitutive effects: The configuration analysis for 2010–2012 reveals clear substitutive effects among configurations 1a, 1b, and 1c. Figure 3 shows that when the regional industrial structure is the core condition, and other conditions are marginal, the R&D investment intensity in configuration 1a, the level of green innovation in configuration 1b, and the combination of R&D human capital, low-carbon consumption awareness, and economic development in configuration 1c can substitute for one another. This suggests that cities lacking sufficient R&D investment can compensate by enhancing green innovation levels or by improving R&D human capital, economic development, and low-carbon awareness. Similarly, improvements in these areas can collectively drive urban low-carbon transformation.

4.2.2. The Second Stage Paths (2012–2017)

In the second stage, the overall consistency and sub-path consistency of the configurations were above the acceptable threshold of 0.75 (as shown in Table 6). This resulted in six distinct pathways, which were categorized into two types (as shown in Table 9).
Landscape-driven: Configurations 2a and 2b emphasize low-carbon consumption awareness and economic development. These two factors complement each other and jointly drive low-carbon transformation. Economic growth provides the resources and incentives needed to support and expand low-carbon initiatives, like green infrastructure and technologies. At the same time, increasing awareness of low-carbon practices encourages both individuals and industries to adopt more sustainable behaviors. This, in turn, stimulates economic growth through demand for eco-friendly products and services. This mutual reinforcement creates a virtuous cycle where economic development and low-carbon awareness enhance each other’s impact, leading to a more effective and sustained urban low-carbon transformation. For example, Nanchang focused on promoting low-carbon consumption awareness and leveraging economic growth. In 2016, the city introduced its “13th Five-Year Ecological Environment Protection Plan”, which set clear low-carbon goals such as reducing carbon emissions, promoting green transportation, and advancing low-carbon industries. This plan guides cities in transitioning from high-carbon industries to service-oriented and technology-driven sectors. Simultaneously, strong economic growth supports these green initiatives and helps optimize industrial structures.
Low-carbon industry and R&D human capital collaboration: Configurations 2c, 2d, and 2e share the same core conditions: regional industrial structure and R&D human capital. Configuration 2c is further supported by R&D investment, low-carbon awareness, and economic development. Configuration 2d is reinforced by economic development, while in configuration 2e, green innovation and low-carbon awareness act as supplementary factors. These additional factors enhance the effectiveness of the core conditions, driving urban low-carbon transformation more efficiently. This pathway emphasizes the collaborative role of expanding low-carbon industries and enhancing R&D human capital. This restructuring increases the presence of low-carbon sectors like renewable energy and green technologies while leveraging R&D to improve sustainability. By integrating R&D capabilities with the shift toward low-carbon sectors, cities can better support economic growth while simultaneously reducing carbon emissions. The collaboration between these two factors reflects how urban areas are strategically balancing technological innovation and industrial development to achieve more effective and sustainable low-carbon transformation. For example, Hangzhou leverages its regional industrial structure and R&D human capital to drive low-carbon transformation. As one of the first pilot cities for new energy vehicles in 2013, Hangzhou focused on promoting electric vehicles. The 2016 “Implementation Measures for Promoting the Construction of New Energy Vehicle Charging Infrastructure” further supports this goal. By integrating green technology development with targeted R&D and policy support, Hangzhou advanced its low-carbon industries and positioned itself as a leader in sustainable urban development.

4.2.3. The Third Stage Paths (2017–2019)

In the third stage, seven configurations were identified, all with consistency levels above 0.96, indicating a 97.8% probability of achieving successful low-carbon transformation (as shown in Table 6). These configurations were classified into two types (as shown in Table 10).
Base and landscape cooperation (configurations 3a, 3b, and 3c): The core conditions in configurations 3a–3c combine niche-level and landscape-level factors. Configuration 3a shows the synergy between R&D human capital and low-carbon consumption awareness. Configuration 3b shows the integration of R&D investment intensity and low-carbon consumption awareness. Configuration 3c shows the combination of R&D human capital and economic development levels. This phenomenon highlights the synergy between niche-level and landscape-level factors. R&D drives technological innovation, while economic growth provides the resources needed for implementation. Simultaneously, raising low-carbon awareness aligns societal behavior with innovation, amplifying its impact. The interaction of these factors across different levels creates a balanced environment that effectively supports low-carbon transformation. For example, Shanghai promotes low-carbon development through the “13th Five-Year Plan for Environmental Protection”. The policy prioritizes optimizing energy structures, boosting efficiency, and supporting green technology innovation. Key R&D projects in renewable energy and smart grids attract talent and accelerated technological progress. Simultaneously, Shanghai has launched a wide range of low-carbon education and publicity activities in schools and communities to strengthen public awareness of environmental protection. These actions create a coordinated effort among policy, technology, and societal participation, driving the widespread adoption of sustainable practices.
Multilayer collaboration (configurations 3d, 3e, 3f, and 3g): In configurations 3d–3g, each configuration includes at least three factors, with configurations 3e and 3f spanning the micro, meso, and macro levels. Configuration 3d shows that reducing high-carbon industries, combined with enhanced low-carbon awareness and economic growth, drives transformation. The core factors in configurations 3e–3g involve low-carbon consumption awareness, regional industrial structure, and R&D investment intensity, with configurations 3e and 3f also incorporating regional innovation levels. The interaction of multiple factors across different levels works together to advance urban low-carbon transformation. This phenomenon highlights the significance of multilayer and multifactor collaboration in urban low-carbon transformation. The configurations demonstrate that successful transformation requires the integration of multiple levels. Additionally, it involves the interplay of various factors, including R&D investment, regional innovation, and economic growth, which together create a comprehensive and adaptive pathway for sustainable urban development. The combined influence of these layers and factors underscores that a holistic approach is essential for driving effective and resilient low-carbon transformation. From 2017 to 2019, Chengdu implemented several key low-carbon initiatives that align with the “multilayer collaboration” pathway by integrating R&D investment, industrial restructuring, and enhanced low-carbon awareness. The “Drive Less, Chengdu Low-carbon e-Travel” program, launched in 2017, encourages residents to reduce car usage through carbon credits, significantly reducing emissions and promoting sustainable transportation. Additionally, Chengdu’s “Park Cities” strategy aims to create green spaces as carbon sinks and enhance ecological resilience. These policies are supported by increased investments in green technology and efforts to shift industries toward lower carbon outputs through electrification and energy efficiency improvements. Configurations 3e and 3f also show substitution effects: When economic development is low, increased R&D human capital can serve as an alternative strategy (as shown in Figure 4).

4.3. Comparative Analysis

The urban low-carbon transformation pathways identified across the three stages highlight significant regional disparities, which can be attributed to differences in socio-economic development, industrial structure, and regional policy priorities. As shown in the chart of the three batches of low-carbon pilot cities, there is a clear pattern of geographical expansion and diversification over time.
Regional characteristics: In the initial phase (2010–2012), the majority of pilot cities were concentrated in more developed regions like Guangdong and Zhejiang, where industrial restructuring and technological advancement are already underway. These cities have relatively higher economic development levels and greater capacity for green innovation, which allows them to focus on reducing high-carbon industries and promoting green technology. In contrast, by the third phase (2017–2019), the pilot program expanded to include cities from less economically developed regions like the western provinces, where low-carbon transformation efforts need to address fundamental challenges such as inadequate infrastructure and reliance on traditional industries. As a result, the pathways in these regions emphasize green R&D and technological innovation, supported by government initiatives and investments.
National policy influence: National policies play a pivotal role in shaping the evolution of low-carbon transformation pathways. In the first phase, the emphasis was mainly on reducing the share of high-carbon industries, reflecting the broader national agenda of industrial restructuring and energy efficiency. As the country’s policy focus changed, the 2012–2017 phase included the introduction of policies like the “Thirteenth Five-Year Plan”, which prioritized ecological development and green innovation. This shift led to new pathways, such as the “Landscape-led” approach, where macro-environmental factors like low-carbon consumption awareness and economic development levels play a more prominent role. By the third phase, national initiatives like the “ecological civilization construction” goals and the “Innovation-Driven Development” strategy significantly raised the stakes for green R&D and technological advancements. This results in pathways where R&D investment intensity and green innovation levels become core conditions driving low-carbon transformation.
Broader socio-economic trends: The socio-economic context also experienced significant changes across the three phases. In 2017, the country’s principal social contradictions shifted from focusing solely on economic growth to emphasizing the quality of life and sustainable development. This societal shift, combined with growing awareness of environmental issues, led to a stronger demand for low-carbon products and practices. Large enterprises like Alibaba and Gree committed publicly to carbon neutrality, which influenced both consumer behavior and corporate practices. The increasing importance of low-carbon consumption awareness, as shown in the configurations of the third phase, highlights how these socio-economic trends reshape the drivers of urban low-carbon transformation. The rapid development of technologies like big data and cloud computing further accelerated the integration of technological innovation into green development strategies, making it a focal point of the third phase.

4.4. Robustness Test

The study conducted a robustness test referencing the criteria proposed by Schneider and Wagemann [59]. This test involved two main approaches: adjusting the calibration intervals and modifying the consistency thresholds.
Calibration interval adjustment: Instead of using the standard 5% and 95% intervals, we used wider intervals of 6% and 96% to assess the impact on the results. This adjustment tested the sensitivity of the conclusions to changes in the calibration intervals.
Consistency threshold modification: The consistency threshold was lowered downward from the original values of 0.8, 0.94, and 0.96 to 0.77, 0.91, and 0.93. This adjustment examined whether variations in the threshold affect the results and conclusions.
The results of the robustness test showed that although there are minor variations in the configurations, the clear subset relationships among the configurations remained unchanged. Consequently, the study’s conclusions are considered robust. This demonstrates that the findings are not highly sensitive to changes in calibration intervals or consistency thresholds, reinforcing the reliability of the research outcomes.
In essence, this robustness test confirmed that the identified configurations and pathways for urban low-carbon transformation are consistent and reliable under variations in the methodology, providing greater confidence in the study’s conclusions.

5. Conclusions and Discussion

5.1. Conclusions

We employed fsQCA method to analyze three periods of low-carbon pilot cities in China, aiming to explore the joint effects of six key factors on urban low-carbon transformation. This analysis identified the core conditions shaping low-carbon transformation and revealed the complex interactions among these factors. Additionally, we conducted a comparative analysis across the three periods to examine the evolving patterns and trends in factor combinations driving urban low-carbon transformation.
There are several key research findings: First, from a single-variable perspective, none of the six antecedent conditions served as bottlenecks for urban low-carbon transformation across the three periods, highlighting the necessity for multiple factors to synergistically contribute. Second, when analyzing the overall pathway types, in the first period, regional industrial structure was identified as the core condition and played a dominant role in driving low-carbon transformation. In the second period, two distinct pathways emerged—one driven by landscape factors and the other characterized by collaboration between low-carbon industries and R&D human capital. By the third period, two types of pathways emerged: cooperative integration of base and landscape factors and multi-layer collaboration. Third, during the second period, several key factors collectively facilitated low-carbon transformation compared to the foundational role of the regional industrial structure in the initial phase. These factors include R&D human capital, low-carbon consumption awareness, and regional economic development. This phase marked an initiation of low-carbon consumption awareness, where interactions across micro, meso, and macro levels began shaping the landscape. By the third period, R&D investment intensity and green innovation became core conditions. While regional industrial structure remains influential, low-carbon consumption awareness gradually evolved into a critical core factor. This phase was characterized by the integration of factors across all levels, leading to increasingly complex and diversified configurations.

5.2. Theoretical Contributions

First, we adopted a configurational perspective in studying low-carbon transformations, addressing a gap in previous studies that predominantly focused on isolated micro-, meso-, or macro-level factors. Prior research has highlighted the importance of integrating resource flows in urban settings but often failed to capture the synergistic effects and asymmetric causal relationships among these levels [60]. Our study shows that multiple pathways for urban low-carbon transformation are possible through different configurations of factors. This approach provides a more comprehensive view of the transformation processes.
Second, our study broadens the application of the MLP framework in urban low-carbon transformation research. By examining the interactions of the macro level, meso level, and micro level, we provide insights into how these layers interact to drive effective transformation. This builds on the work of other scholars who have highlighted the need for integrated approaches in sustainable urban development but often overlooked the combined effects of these levels [59]. Our research deepens the understanding of how these factors converge to create effective low-carbon pathways across different periods.
Third, we performed a cross-case comparative analysis that revealed the temporal evolution of low-carbon transformation pathways across three distinct periods. This dynamic approach offers a more detailed understanding of how core factors change over time, providing deeper insights into the shifting configurations that drive transformation. Such cross-period analyses are essential for identifying patterns and addressing the complexities of urban transformation, as noted in prior studies on urban resilience and sustainability [61].
Finally, we employed dynamic QCA methods in our study, extending and enriching the methodological approach in low-carbon research. By analyzing the evolution of configurations over time, our study aligns with the growing international focus on developing dynamic QCA methods [62]. This contributes to the broader theoretical foundation by offering practical guidance for urban low-carbon transformation and informing policy decisions.

5.3. Practical Inspiration

Our findings provide policymakers with enhanced insights into the systematic and complex dynamics of urban low-carbon transformation.
First, the pathways of urban low-carbon transformation are increasingly complex and diversified. Effective transformation requires multi-level collaboration across the macro, meso, and micro levels. Consequently, a one-size-fits-all approach is not appropriate. Each city should consider its unique conditions and local resources to establish customized low-carbon transformation pathways or models.
Second, the regional industrial structure consistently plays a central role in the configurations across all three phases. This underscores the critical importance of low-carbon industries. Policymakers need to address the issue of reliance on high-carbon industries such as coal. There should be increased investment in developing low-carbon technologies and promoting the growth of new energy industries such as photovoltaic power generation and electric vehicles while gradually reducing dependency on high-carbon sectors.
Third, as low-carbon consumption awareness continues to grow across the three phases, regions should consistently promote environmentally friendly initiatives and allocate more resources to environmental awareness campaigns. Policymakers should strengthen low-carbon consumption awareness and foster low-carbon consumer preferences. These actions can drive businesses to undertake low-carbon transformations and provide the necessary momentum for innovation in low-carbon practices.

5.4. Limitations and Future Research

Our research has three main limitations. First, although qualitative data are included, the study still encountered the common challenge in QCA research of deepening the qualitative analysis. Further in-depth exploration of the qualitative data could strengthen the research findings. Second, the measurement of low-carbon consumption awareness primarily focused on government-provided green commuting options, such as dedicated lanes for vehicles. However, other green commuting options available in various cities, such as shared bicycles, electric bikes, and green-plated private cars, also influence consumer behavior, even though they are not directly facilitated by the government. Future research could incorporate a broader range of data on green commuting options to achieve a more comprehensive assessment of low-carbon consumption awareness. Third, this study compared data from three periods, but inconsistencies in publication timing across the batches of low-carbon pilot cities created variations in case data. These variations complicated cross-time comparisons and may weaken case support. Future research should explore methods for stronger case comparisons.

Author Contributions

Conceptualization, Y.-D.L. and C.-L.Y.; methodology, C.-L.Y.; software, C.-L.Y.; validation, Y.-D.L. and C.-L.Y.; formal analysis, Y.-D.L.; investigation, Y.-D.L.; resources, Y.-D.L.; data curation, C.-L.Y.; writing—original draft preparation, Y.-D.L.; writing—review and editing, Y.-D.L.; visualization, C.-L.Y.; supervision, Y.-D.L.; project administration, Y.-D.L. and C.-L.Y. funding acquisition, Y.-D.L. All authors have read and agreed to the published version of the manuscript.

Funding

National Natural Science Foundation of China project “Research on decision optimization and coordination of low carbon supply chain considering equity cooperation under carbon regulation” (Project No. 72062023); Inner Mongolia Autonomous Fund for Distinguished Young Scholars “Research on complex mechanisms and differentiated paths of doing business in ethnic areas to enhance urban economic resilience” (Project No. 2024JQ19); Inner Mongolia Autonomous Region universities innovation team development plan support project “Big data and green governance” (NMGIRT 2202); ”Doing Business and High-Quality Development of Private Enterprises” research base.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

We would like to acknowledge Wen Zhang (participated in writing, technical editing of the manuscript, and language editing) and Han Wang (participated in the design of methodology, supervision, and investigation).

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 16 07630 g001
Figure 1. Urban low-carbon transformation framework.
Figure 1. Urban low-carbon transformation framework.
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Figure 2. Low-carbon pilot cities in China. Note: The three batches represent the low-carbon pilot cities announced in 2010, 2012, and 2017, respectively, excluding cities that are repeated from previous batches.
Figure 2. Low-carbon pilot cities in China. Note: The three batches represent the low-carbon pilot cities announced in 2010, 2012, and 2017, respectively, excluding cities that are repeated from previous batches.
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Figure 3. Substitution relationships among configurations 1a, 1b, and 1c. Note: The text at the top of the image indicates that the conditions and their effects are common to all three configurations.
Figure 3. Substitution relationships among configurations 1a, 1b, and 1c. Note: The text at the top of the image indicates that the conditions and their effects are common to all three configurations.
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Figure 4. Substitution relationship between configuration 3e and configuration 3f.
Figure 4. Substitution relationship between configuration 3e and configuration 3f.
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Table 1. Three batches of low-carbon pilot cities (prefecture level and above).
Table 1. Three batches of low-carbon pilot cities (prefecture level and above).
PeriodBatch Pilot Cities
2010–2012First Batch Guangzhou, Shenyang, Wuhan, Xi’an, Kunming, Tianjin, Chongqing, Shenzhen, Xiamen, Hangzhou, Nanchang, Guiyang, and Baoding
2012–2017Second Batch Beijing, Shanghai, Haikou, Shijiazhuang, Qinhuangdao, Jincheng, Hulunbuir, Jilin, Suzhou, Huai’an, Zhenjiang, Ningbo, Wenzhou, Chizhou, Nanping, Jingdezhen, Qianzhou, Qingdao, Guilin, Guangyuan, Zunyi, Yan’an, Jinchang, and Urumqi
2017–2019Third Batch Wuhai, Dalian, Chaoyang, Nanjing, Changzhou, Jiaxing, Jinhua, Quzhou, Hefei, Huaibei, Huangshan, Lu’an, Xuancheng, Sanming, Ji’an, Fuzhou, Jinan, Yantai, Weifang, Changsha, Zhuzhou, Xiangtan, Chenzhou, Zhongshan, Liuzhou, Sanya, Chengdu, Yuxi, Ankang, Lanzhou, Xining, Yinchuan, and Wuzhong
Note: Data from the National Development and Reform Commission of China.
Table 2. Measurement standards and data sources.
Table 2. Measurement standards and data sources.
Conditions and ResultMeasurement StandardData Sources
Urban carbon intensity
(UCI)		(Carbon emissions from natural gas + coal gas + liquefied petroleum gas + electricity + heat energy)/Regional GDPChina Urban Statistical Yearbook
China Energy Statistical Yearbook
R&D investment intensity
(R&D II)		(R&D expenditure + education expenditure)/Regional GDPChina Urban Statistical Yearbook
R&D human capital
(R&D HC)		R&D personnel full-time equivalentChina Science and Technology Statistical Yearbook
Regional industrial structure
(RIS)		Rate of change in the ratio of value added in the secondary industry to regional gross domestic productChina Urban Statistical Yearbook
Green innovation level
(GIL)		Number of green patent authorizations of listed companies in each cityNational Intellectual Property Administration
Low-carbon consumption awareness (LCCA)Public trams available at the end of the year/(Public trams available at the end of the year + taxis available at the end of the year)China Urban Statistical Yearbook
Economic development level (EDL)Per capital regional gross domestic product in the whole cityChina Urban Statistical Yearbook
Table 3. Descriptive statistics.
Table 3. Descriptive statistics.
Conditions and OutcomeMeanVarianceMinimumMaximumSample Size
R&D II0.1910.0370.1200.28970
R&D HC21.88022.0930.547680.32170
RIS51.0299.76522.14078.37070
GIL48.24335.5781.000134.00070
LCCA0.3790.1280.1260.78170
EDL8.6794.0662.87620.34970
UCI0.5920.5220.2143.11570
Table 4. Calibration of conditions and results.
Table 4. Calibration of conditions and results.
Conditions and Results2010–20122012–20172017–2019
Full MembershipCrossover
Point		Non-membershipFull MembershipCrossover PointNon-membershipFull MembershipCrossover PointNon-membership
R&D II0.2110.1810.1330.2480.1900.1120.2640.1900.136
R&D HC37.9387.4292.75851.5659.4762.51163.52815.7282.490
RIS29.95035.23043.92834.46047.63067.01637.90251.21065.944
GIL25.6009.0002.200120.248.0008.000108.5546.0002.000
LCCA0.5300.3760.1720.4930.3730.2040.5580.3810.205
EDL10.2726.8322.77514.2666.4162.91516.1107.8163.475
UCI0.2810.4470.6910.2510.4051.1030.2450.3971.623
Table 5. Necessity analysis results.
Table 5. Necessity analysis results.
Antecedent Condition2010–20122012–20172017–2019
ConsistencyCoverageConsistencyCoverageConsistencyCoverage
R&D II0.7770.7240.6690.7060.5250.660
Non-R&D II0.6980.6170.6470.7210.6530.731
R&D HC0.7030.6790.6520.7890.4540.623
Non-R&D HC0.7940.6780.6300.6180.6820.709
RIS0.6740.6270.6990.7810.5620.654
Non-RIS0.7650.6770.6350.6680.6160.743
GIL0.7200.6740.6230.6710.5270.660
Non-GIL0.7370.6480.6680.7290.5760.647
LCCA0.7150.6770.6820.8060.4530.553
Non-LCCA0.7760.6760.6030.6040.6700.769
EDL0.6600.6740.6780.7220.5030.613
Non-EDL0.7590.6190.5820.6420.6430.738
Table 6. Configurations 1 and 2 of urban low-carbon transformation.
Table 6. Configurations 1 and 2 of urban low-carbon transformation.
DimensionsTime (Number)2010–2012 2012–2017
Configuration1a1b1c2a2b2c2d2e
Micro-level nicheR&D IISustainability 16 07630 i001Sustainability 16 07630 i002Sustainability 16 07630 i001Sustainability 16 07630 i001Sustainability 16 07630 i001Sustainability 16 07630 i002Sustainability 16 07630 i001Sustainability 16 07630 i001
R&D HCSustainability 16 07630 i003Sustainability 16 07630 i003Sustainability 16 07630 i002 Sustainability 16 07630 i002Sustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i004
Meso-level system layerRISSustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i003 Sustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i004
GILSustainability 16 07630 i002Sustainability 16 07630 i003Sustainability 16 07630 i003Sustainability 16 07630 i003Sustainability 16 07630 i003 Sustainability 16 07630 i003Sustainability 16 07630 i002
Macro-level landscapeLCCASustainability 16 07630 i003Sustainability 16 07630 i003Sustainability 16 07630 i002Sustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i002Sustainability 16 07630 i003Sustainability 16 07630 i002
EDLSustainability 16 07630 i003Sustainability 16 07630 i003Sustainability 16 07630 i002Sustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i002Sustainability 16 07630 i002Sustainability 16 07630 i003
Consistency0.8600.7950.9930.9470.9390.9760.9840.968
Original coverage0.3050.2200.1930.2870.2910.3270.2450.212
Unique coverage0.1580.0880.1010.0500.0120.0700.0730.046
Overall coverage0.4940.548
Overall consistency0.8700.934
Note: Sustainability 16 07630 i004 = core causal condition present; Sustainability 16 07630 i001 = core causal condition absent; Sustainability 16 07630 i002 = peripheral condition present; Sustainability 16 07630 i003 = peripheral condition absent.
Table 7. Configuration 3 of urban low-carbon transformation.
Table 7. Configuration 3 of urban low-carbon transformation.
DimensionsTime (Number)2017–2019
Configuration3a3b3c3d3e3f3g
Micro-level nicheR&D IISustainability 16 07630 i001Sustainability 16 07630 i004Sustainability 16 07630 i001Sustainability 16 07630 i001Sustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i004
R&D HCSustainability 16 07630 i004Sustainability 16 07630 i001Sustainability 16 07630 i004 Sustainability 16 07630 i002
Meso-level system layerRISSustainability 16 07630 i003Sustainability 16 07630 i002Sustainability 16 07630 i002Sustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i004
GILSustainability 16 07630 i003 Sustainability 16 07630 i003Sustainability 16 07630 i003Sustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i003
Macro-level landscapeLCCASustainability 16 07630 i004Sustainability 16 07630 i004 Sustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i004
EDL Sustainability 16 07630 i002Sustainability 16 07630 i004Sustainability 16 07630 i004Sustainability 16 07630 i002 Sustainability 16 07630 i003
Consistency0.9900.9680.9930.9780.9830.9890.972
Original coverage0.2420.2450.2550.2280.3080.1870.364
Unique coverage0.0210.0100.0300.0150.0040.0120.026
Overall coverage0.533
Overall consistency0.978
Note: Sustainability 16 07630 i004 = core causal condition present; Sustainability 16 07630 i001 = core causal condition absent; Sustainability 16 07630 i002 = peripheral condition present; Sustainability 16 07630 i003 = peripheral condition absent.
Table 8. Antecedent condition for configuration 1.
Table 8. Antecedent condition for configuration 1.
TypeConfiguration 1Multi-Factor Combination Paths
Low-carbon industry-driven1aRIS * + GIL
1bRIS * + R&D II
1cRIS * + R&D HC + LCCA + EDL
Note: * core causal condition present; other conditions peripheral condition present.
Table 9. Antecedent condition for configuration 2.
Table 9. Antecedent condition for configuration 2.
TypesConfiguration 2Multi-Factor Combination Paths
Landscape-driven2aLCCA * + EDL *
2bLCCA * + EDL * + R&D HC
Low-carbon industry and R&D human capital collaboration2cR&D HC * + RIS * + R&D II + LCCA + EDL
2dR&D HC * + RIS * + EDL
2eR&D HC * + RIS * + GIL + LCCA
Note: * core causal condition present; other conditions peripheral condition present.
Table 10. Antecedent condition for configuration 3.
Table 10. Antecedent condition for configuration 3.
TypesConfiguration 3Multi-Factor Combination Paths
Base and landscape cooperation3aR&D HC * + LCCA *
3bR&D II * + LCCA * + RIS + EDL
3cR&D HC * + EDL * + RIS
Multilayer collaboration3dRIS * + LCCA * + EDL *
3eRIS * + GIL * + LCCA * + R&D II * + EDL
3fRIS * + GIL * + LCCA * + R&D II * + R&D HC
3gRIS * + LCCA * + R&D II *
Note: * core causal condition present; other conditions peripheral condition present.
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Li, Y.-D.; Yan, C.-L. Driving Paths and Evolution Trends of Urban Low-Carbon Transformation: Configuration Analysis Based on Three Batches of Low-Carbon Pilot Cities. Sustainability 2024, 16, 7630. https://doi.org/10.3390/su16177630

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Li Y-D, Yan C-L. Driving Paths and Evolution Trends of Urban Low-Carbon Transformation: Configuration Analysis Based on Three Batches of Low-Carbon Pilot Cities. Sustainability. 2024; 16(17):7630. https://doi.org/10.3390/su16177630

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Li, You-Dong, and Chen-Li Yan. 2024. "Driving Paths and Evolution Trends of Urban Low-Carbon Transformation: Configuration Analysis Based on Three Batches of Low-Carbon Pilot Cities" Sustainability 16, no. 17: 7630. https://doi.org/10.3390/su16177630

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