
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)11600-3
10.1016/j.heliyon.2024.e35569
e35569
Research Article
Is smart city low-carbon? Evidence from China
Zhu Xiongwei 230208630@seu.edu.cn
a
Li Dezhi njldz@seu.edu.cn
ab⁎
Zhu Shiyao syzhu@mail.ubc.ca
c
Ting ShiAn e0536993@u.nus.edu
d
a Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, 210018, China
b Engineering Research Center of Building Equipment, Energy, and Environment, Ministry of Education, Southeast University, Nanjing, 210018, China
c Department of Wood Science, The University of British Columbia, Vancouver, V6T 1Z4, Canada
d Department of the Built Environment, College of Design and Engineering, National University of Singapore, Singapore, 117566, Singapore
⁎ Corresponding author. Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, 210018, China. njldz@seu.edu.cn
02 8 2024
30 8 2024
02 8 2024
10 16 e3556923 3 2024
30 7 2024
31 7 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Smart cities were originally conceived to address a myriad of urban challenges arising from rapid urbanization, including energy scarcity, congestion, and environmental degradation. The Chinese government has made substantial efforts to advance smart city initiatives. However, the extent to which the integration of smart technologies contributes to urban sustainability, especially within a high-carbon urbanization paradigm, poses a critical question in light of escalating extreme weather events and worsening global challenges. Urgency is underscored in prioritizing low-carbon strategies within smart city frameworks. This paper presents a Multicriteria Decision Making Network (MCDN) approach to assess and rank the low-carbon levels (LCL) of 36 pilot smart cities in China. Findings reveal that overall LCL among these cities remains relatively modest, with significant disparities attributed to varying economic, social, institutional, cultural, and environmental contexts. The study also delves into the nexus between urban intelligence and LCL, highlighting a discernible positive correlation between a city's smartness and its low-carbon profile. Moreover, empirical evidence suggests that advancements in smart technologies are conducive, albeit to varying degrees, to enhancing urban LCL. In light of these findings, recommendations are made to fortify economic and social advancement, bolster management practices, and foster multi-stakeholder collaboration to propel the coordinated development of smart and low-carbon initiatives in China.

Keywords

Smartness
Low-carbon level
AHP-TOPSIS method
Regression analysis
==== Body
pmc1 Introduction

Driven by globalization and technological innovation, the pace of urbanization is accelerating, with an increasing number of people choosing to live in cities. According to United Nations projections, by 2050, nearly 70 % of the global population will reside in urban areas (United Nations, 2023) [1]. Although this dense migration to urban regions has spurred economic development and social progress, it also brings a series of challenges and issues, including resource scarcity (such as clean water and sustainable energy supplies), environmental degradation (including air and water pollution), infrastructure overload (such as traffic congestion and insufficient public services), social inequalities (including wealth gaps and housing shortages), and extreme weather events caused by climate change (such as floods, heatwaves, and droughts) [2]. For example, the COVID-19 pandemic, which broke out globally in 2020, had a massive impact on urban life and the global economy, exposing the weaknesses of urban public health systems and emergency management capabilities. The flood disasters in Germany and Belgium in 2021 highlighted the vulnerability of urban infrastructure to extreme climate events. Therefore, as a complex entity encompassing diverse populations, intricate economic activities, and dense infrastructure, cities must seek new methods and strategies to enhance their capacity to address these challenges, providing a healthy, safe, and prosperous living environment for urban residents.

The construction of smart cities is regarded as a crucial strategy for addressing the challenges of rapid urbanization and promoting urban sustainable development. By integrating Information and Communication Technology (ICT) with innovative urban management concepts, the aim of smart cities extends beyond optimizing city management and services, placing a special emphasis on enhancing environmental sustainability, economic efficiency, and social welfare. This includes leveraging intelligent data analysis to optimize resource consumption, increase energy efficiency, reduce waste, and strengthen urban resilience to climate change [3]. In recent years, smart city projects have increasingly focused on utilizing technological innovations to foster green growth and environmental protection, reflecting a shift from reliance solely on technology to a comprehensive consideration of human, social, and ecological factors [4]. Smart cities no longer focus exclusively on technology itself but rather on how technology can enable the coordinated development of various urban aspects and improve the quality of life for residents [5]. Measures such as smart energy management systems, intelligent transportation solutions, and sustainable urban planning have been proven globally to effectively address urbanization challenges and drive cities towards more sustainable, livable, and inclusive directions [6]. Currently, the ISO 37122 standard, ITU-T Y.49303/L.1603 standard, and the smart city framework developed by Boyd have become important tools for evaluating and enhancing the construction of smart cities worldwide [7].

Urban areas, serving as pivotal hubs for economy, society, and daily life, are responsible for approximately 80 % of global carbon emissions [8]. These emissions not only disrupt the delicate balance of the global climate system but also exacerbate the urban heat island effect, posing significant health risks to urban populations [9]. Presently, global warming presents cities with one of their most pressing challenges. According to projections by the Intergovernmental Panel on Climate Change (IPCC), sea levels are anticipated to rise by 0.6–1.1 m by the end of this century. Moreover, extreme weather events triggered by global warming phenomena, such as El Niño and La Niña, are forecasted to displace approximately 280 million people worldwide [10]. Hence, there is a pressing need to ascertain whether smart cities can effectively advance urban low-carbon development, as intended. Low-carbon urban construction emphasizes the reduction of carbon emissions and the augmentation of carbon sinks within urban systems, aiming for sustainable development of the “socio-economic-natural” dual urban structure [11]. Smart cities, leveraging Information and Communication Technology (ICT), aim to optimize urban energy consumption management, thereby curbing greenhouse gas emissions and enhancing overall energy efficiency [12]. However, the substantial information infrastructure associated with smart city construction, including data centers, communication networks, and cloud computing platforms, consumes a significant amount of electricity, often derived from fossil fuel sources, leading to substantial carbon emissions. Hence, while smart city initiatives strive to enhance urban management efficiency and residents' well-being, equal emphasis must be placed on mitigating their operational carbon footprint through optimized resource utilization and energy consumption reduction. This approach enables cities to progress towards a more environmentally sustainable and low-carbon trajectory. In essence, exploring synergies between smart and low-carbon urban development models holds paramount importance in achieving urban sustainability goals [13]. Concurrently, investigating experiences of coordinated development in typical smart and low-carbon urban projects serves as a crucial step towards fostering comprehensive national urban intelligence and low-carbon coordinated development strategies.

The reminder of this paper is organized as follows. Section 2 provides a critical literature review. Section 3 introduces the research methods. Section 4 displays the analysis results by applying the data collect from 36 typical cities in China. Section 5 presents the discussions on the analysis results, followed by section 6 which draws the conclusions of this study.

2 Literature review

In recent years, research on smart cities and low-carbon cities has emerged as a focal point, with a substantial body of related studies being conducted. This section provides an overview of the research topics concerning smart cities and low-carbon cities, as well as a correlation analysis between smart cities and low-carbon cities.

2.1 Researches of the smart city

The rise of smart cities is a response to the challenges of rapid urbanization, aiming to enhance urban management efficiency and residents' quality of life through advanced information and communication technologies (ICT) [14]. With the development of big data, the Internet of Things (IoT), and cloud computing, smart cities have evolved from basic informatization stages to a new phase that emphasizes data-driven decision-making, personalized urban services, and intelligence. This transformation is not only aimed at addressing urban issues such as traffic, environment, and resource management but also explores how technological innovation can promote sustainable urban development and improve residents' welfare [15]. Current research on smart cities is multidimensional. Some scholars believe that the development of smart cities primarily involves using advanced ICT to optimize urban functions and manage the social, economic, and cultural challenges that arise during the urbanization process [16]. Other scholars argue that the construction of smart cities today requires not only technological innovation but also consideration of social, economic, and environmental sustainability [17]. This includes ensuring the inclusivity and equity of smart city technologies and balancing technology development with environmental protection [18].

Globally, China has made significant progress in smart city development. Since the concept of smart cities emerged in the 1990s, the Chinese government has consistently regarded it as a strategic priority, promoting smart city construction through substantial investments and policy support [19]. As of April 2020, the Chinese government has announced 789 smart city pilots, placing China at the forefront in terms of the number of smart city pilots worldwide [20]. Smart city initiatives have encompassed various domains, including intelligent transportation systems, smart government administration, and smart healthcare, resulting in numerous smart city clusters in areas like the Yangtze River Delta and the Pearl River Delta [21]. Despite the considerable achievements in smart city development, China still faces challenges such as insufficient green construction and low-carbon development, uneven quality of infrastructure construction, inadequate cross-sector collaboration and data sharing, and limited citizen participation and democratic decision-making. To bridge the gap with typical developed countries and guided by the “evaluation to promote construction” philosophy, since 2011, the Information Center of the Chinese Academy of Social Sciences, in collaboration with Guomai Interconnect Smart City Research Center, has conducted annual evaluations of smart cities and made the assessment reports public. These reports evaluate the construction level of China's smart pilot cities from five aspects: smart infrastructure, smart governance, smart people's livelihoods, smart economy, and safety, using 17 communication technology-based indicators. The evaluation results have been widely recognized by researchers both domestically and internationally. In this study, the smartness of sample cities will be adopted from this assessment report.

2.2 Researches of the low-carbon city

Since the concept of “low-carbon city” was first introduced in the UK's 2003 energy white paper “Our Energy Future: Creating a Low Carbon Economy”, research on low-carbon cities has been a hot topic in academia. Current research themes on low-carbon cities primarily cover six aspects: urban low-carbon scale (macro-scale, meso-scale, and micro-scale), urban low-carbon energy (solar energy, wind energy, deep geothermal energy, etc.), urban low-carbon behavior (government behavior, business behavior, residential behavior), urban low-carbon policy (economic policy, energy policy, social policy, etc.), urban low-carbon mobility (electric vehicles, shared mobility, spatial planning, etc.), and urban carbon sinks (green space rate, forest age, forest species structure, etc.) [22,23]. These studies often take a complex urban system perspective that encompasses the city's economy, society, management, and ecology, highlighting the importance of planning and design, energy transition, decision support, and management and policy for the future construction of low-carbon cities. Additionally, current evaluation research related to low-carbon cities is mainly divided into two approaches: the first is low-carbon city single-criterion evaluation systems (urban low-carbon policy, urban low-carbon economy, urban low-carbon energy, etc.); the other is a comprehensive multi-criteria evaluation system for low-carbon cities (overall urban low-carbon development level) [24].

As of the end of 2023, China's carbon emissions accounted for 29.51 % of the global total, remaining the largest carbon emitter in the world [25]. Compared to developed countries, China is in a rapid phase of industrialization and urbanization, with energy and industrial emissions being the primary sources of carbon emissions, followed by the construction and transportation sectors. Throughout its prolonged industrialization and urbanization, China has increasingly faced issues such as resource supply and demand contradictions and ecological environmental damage [26]. Consequently, building low-carbon cities has become an important approach for China to construct a resource-conserving, environmentally friendly society and a strategic choice for comprehensively building a moderately prosperous society [27]. Since the Ministry of Construction and the World Wildlife Fund initiated low-carbon pilot city projects in Shanghai and Baoding in 2008, the Chinese government has launched three batches of low-carbon pilot city projects, totaling 81 cities covering a broad area from the eastern coast to the western inland. However, in China, both research and practice regarding low-carbon cities are generally still in the stages of original policy exploration and innovation, lacking a clear construction system and legal support for low-carbon cities, as well as a complete evaluation system and assessment mechanism. Moreover, the current construction of low-carbon cities in China is significantly disjointed from other national development strategies such as smart cities, with serious issues of unbalanced and insufficient development in the low-carbon city construction sector and notably low public and societal participation [28].

2.3 Researches on the correlation analysis between smartness and LCL

Research on the correlation between urban smartness and LCL has seen increasing interest from scholars in recent years, establishing it as a new focal point within the domain. Methods commonly employed for analyzing variable correlations include the Pearson correlation coefficient, Spearman's rank correlation coefficient, Kendall's tau, partial correlation, point-biserial correlation, and multiple correlation [29,30]. Discussions in the academic community regarding the correlation between smartness and LCL mainly highlight two perspectives. One viewpoint suggests that smartness and LCL are unrelated. Some scholars argue that smartness and LCL prioritize different aspects, with smartness focusing on urban technological and economic advancements, whereas LCL emphasizes urban ecological development [31]. De Jong, for instance, identified 12 urban development concepts including smart city, low-carbon city, eco-city, and green city, arguing that clear distinctions must be made among these urban types to accurately guide future urban planning [32]. Another viewpoint posits a correlation between smartness and LCL. Within this group, some scholars assert a positive relationship between smartness and LCL, proposing that smartness can promote low-carbon development. For example, smart systems developed by smart city construction, such as smart grids and intelligent transportation networks, can effectively match urban energy supply and demand, thus reducing urban carbon emissions. Conversely, another segment of scholars views the relationship between smartness and LCL as negatively correlated [33]. Deakin, for example, believes the direct environmental benefits from IoT technologies are not sufficient to meet urban sustainable development goals [34]. Additionally, Barr et al. contend that smart city logic often prompts city administrations to focus on superficial changes and promote individual behavioral shifts, which detracts from the essential task of reconfiguring urban infrastructure for low-carbon lifestyles [35].

In conclusion, while existing research has made significant contributions to the individual assessment and progress of urban smartness and LCL, a notable gap remains in understanding their concurrent development. This gap presents an opportunity for further investigation into the combined potential and integrated strategies for smart and low-carbon urban development. Current efforts to enhance urban smartness or low-carbon levels often operate in silos, with limited consideration of a holistic view that encompasses both aspects. Moreover, the operational tools to support multi-objective strategies are still nascent. To address this gap, this study proposes the establishment of an evaluative framework for assessing urban low-carbon levels and explores the intricate relationship between urban smartness and low-carbon levels in China. The aim is to provide actionable insights for the advancement of smart and low-carbon city construction. The objectives of this paper are as follows: (i) to identify indicators for evaluating urban low-carbon levels; (ii) to assess the low-carbon levels of China's smart cities using a multi-criteria decision-making (MCDM) analysis; (iii) to uncover the correlation between a city's low-carbon levels and its smartness; and (iv) to propose strategies for enhancing both the smartness and low-carbon levels of cities.

3 Methodology

This study aims to evaluate the urban low-carbon level and explore the relationship between urban smartness and LCL, which has often been overlooked by scholars. Given the lack of standardized indicators for LCL, the MCDM methods not only enable a comparative evaluation but also assist policymakers in identifying and implementing optimal strategies. To this end, this study propose to create an indicator-based measurement that combines AHP and TOPSIS. In this paper, this hybrid approach is designed for the comparative analysis of the urban LCL in Chinese smart pilot cities. The methodology consists of four steps, as illustrated in Fig. 1. Firstly, a suitable conceptual framework is established to define indicators. Secondly, relevant indexes are identified based on existing literature and government reports. Next, the hybrid AHP-TOPSIS method is employed to weigh the indexes and rank the sample smart cities using MCDM. Finally, the relationship between smartness and LCL were tested by Pearson correlation analysis.Fig. 1 Methodology flowchart.

Fig. 1

3.1 Selection of conceptual framework

The research on the development of low-carbon cities is a prominent and actively pursued area, with numerous institutions and scholars contributing significantly. Current researches on low-carbon city mainly focus on six aspects, as shown in Table 1.Table 1 The current evaluation perspectives of low-carbon cities.

Table 1Low-carbon cities evaluation perspectives	Justification	
(1) The evaluation of low-carbon cities is carried out within the sustainability framework

	[36]	
(2) The assessment of low-carbon cities is based on their unique urban characteristics

	[37]	
(3) Factor analysis is utilized for evaluating low-carbon cities

	[38]	
(4) The evaluation of low-carbon cities is grounded in the construction of ecological civilization

	[39]	
(5) The evaluation of low-carbon cities is based on the components of carbon source and carbon sink

	[40]	
(6) The evaluation of low-carbon cities is centered around maximizing city value

	[41]	

A multitude of scholars and institutions have tirelessly pioneered the development of innovative assessment frameworks dedicated to the advancement of low-carbon cities. Notable frameworks include the Pressure-State-Response (PSR) framework [41], the Driving force-Statue-Response (DSR) framework [42], the Exposure-Resilience-Sensitivity (ERS) framework [43], the Adaptive Capacity Index (ACI) [44], the Force-Pressure-State-Impact-Response (DPSIR) framework [45], and the Tourism-Based urban destinations (TUBDs) [46]. Nevertheless, due to the multiplicity of theoretical foundations, the establishment of clear and standardized procedures for selecting final indicators is a challenging task. The majority of scholars strive to integrate sound theoretical underpinnings for variable selection, weighting, and aggregation [47]. In this study, we have adopted the DPSIR framework model, which is an evaluative model proposed by the OECD, organized around causal relationships to structure related indicators. It has been developed by integrating the strengths of both the PSR and DSR models. This framework is widely applied in the assessment of complex environmental systems and has progressively become an effective tool for determining the status of systems and the causal relationships of their problems. This choice is also the first attempt to convert the theoretical framework into operational practice.

The formation mechanism and construction process of low-carbon cities reflect the complex dynamic interactions between human activities and the sustainability of urban environments. Within the DPSIR conceptual framework, “Driving Forces” identify the socio-economic and environmental factors that promote the development of low-carbon cities, primarily including concern for global climate change, advancements in renewable energy technologies, and societal demand for sustainable living environments; “Pressure” refers to the stress on the environment and urban systems caused by these driving forces, mainly involving greenhouse gas emissions from conventional energy sources, urban expansion, and the consumption of non-renewable resources; “State” assesses the current state of the urban environment in relation to low-carbon goals, covering aspects such as air and water quality, patterns of energy consumption, and the carbon footprint of urban activities; “Impact” analyzes the effects of the current state of urban carbon emissions on public health, economic vitality, and ecological integrity, including positive effects such as improved air quality and reduced health risks, as well as potential challenges in transitioning to renewable energy sources and managing economic transformation; “Response” identifies and implements measures to support the development of low-carbon cities, including policy measures, technological innovations, urban planning strategies, and community engagement initiatives [48].

Therefore, the four dimensions for evaluating the level of low-carbon city construction in this study are low-carbon economic, low-carbon society, low-carbon environment quality, and low-carbon management. Among them, the low-carbon economic dimension focuses on the relationship between carbon emissions and financial aspects such as scale, speed, and structure [49]. Low-carbon society encompasses factors related to quality of life, population, and infrastructure in a city [50]. Low-carbon environment quality primarily considers the living environment of residents, including factors like environmental conditions, pollution, and regulations. Lastly, low-carbon management represents the policies and measures formulated for the construction of low-carbon cities and the optimization of urban planning.

3.2 Identification of low-carbon indicators

The identification of indicators for smart city and low-carbon city construction levels typically encompasses social, economic, environmental, and governmental dimensions. However, there are differences in specific indicators between the two development models, as highlighted by Peng et al. [48]. In this study, ICT-based indexes were used to evaluate the smartness of sample cities based on Chinese government reports, as shown in Table A4. To ensure scientific rigor in examining the relationship between smart cities and low-carbon cities, non-ICT-based indicators were chosen for low-carbon evaluation. The proposed indicators for evaluating LCL of sample cities can be found in Appendix A-Table A1.

To select appropriate indicators that suit the specific development context of China and ensure data availability, the Experts Grading method was employed. Eight experts with extensive experience in smart city and low-carbon city construction were invited to participate. The experts’ details are presented in Table 2. Based on their grading, the final evaluation indicators for low-carbon construction level are determined and listed in Table 3, along with their roles and justifications. In addition, the Min-Max rescaling method was used to normalize positive and negative indicators, where a higher value indicates better performance for positive indexes, while a lower value indicates better performance for negative indexes [51].Table 2 Detailed profiles of the experts.

Table 2Experts	Age	Gender	Organization	Role	Years	
A	42	Male	Construction company	General manager	12	
B	43	Male	Consultant company	General manager	11	
C	48	Male	University	Professor	15	
D	53	Female	University	Professor	21	
E	39	Female	University	Professor	8	
F	52	Male	University	Professor	18	
G	49	Male	Construction bureau in government	Director	17	
H	47	Female	Municipal bureau in government	Director	15	

Table 3 Final-LCL indicators after experts’ grading.

Table 3Indicators	Description	Categories	Justification	
Low-carbon economic	
LCE1: Per capital GDP	Per capita gross domestic product (yuan)	Driving force	[58]	
LCE2: Energy Consumption per GDP	Energy consumption per unit of GDP (yuan/KW)	Driving force	[59]	
LCE3: Industrial capacity utilization	The ratio of total industrial output to production equipment (%)	Driving force	[60]	
LCE4: Proportion of industry primary energy in total energy consumption	The Proportion of primary energy such as natural gas in total energy consumption (%)	Driving force	[28]	
LCE5: Proportion of industry coal burning in primary energy consumption	The proportion of coal burning in primary energy consumption in industry (%)	Driving force	[61]	
LCE6: Industrial electricity consumption	Enterprises in large-scale production and processing industries use electricity (Million KWH)	Driving force	[62]	
LCE7: Proportion of secondary industry in GDP	The value created by industrial production as a percentage of GDP (%)	Driving force	[63]	
LCE8: Proportion of tertiary industry in GDP	The share of GDP generated by services or businesses (%)	Driving force	[64]	
LCE9: Proportion of high-tech industry added value in added value of industries above designated size in the whole city	Industries with high knowledge and technology intensity and rapid development accounted for the proportion of the city's total industries (%)	Driving force	[65]	
Low-carbon society	
LCS1: Urbanization rate	The proportion of urban population in total population (including agricultural and non-agricultural) (%)	Status	[66]	
LCS2: R&D proportion	The proportion of scientific research and experimental development (%)	Status		
LCS3: Urban Road area per capita	The area of a city's roads divided by the number of people in that city (m2)	Status	[67]	
LCS4: Traffic volume	Number of vehicles on urban roads (YoY)	Status	[68]	
LCS5: Engel coefficient	The proportion of food expenditure in household total consumption expenditure (%)	Status	[69]	
LCS6: Population density	The number of people per unit of land area	Status	[70]	
LCS7: Per capita consumption of electricity	The amount of electricity consumed per person per year by the average household in a country or region (KWH)	Status	[71]	
LCS8: Gas penetration in cities	The ratio of the number of people using natural gas to the total population of the city (%)	Status	[72]	
Low-carbon environment quality	
LCEQ1: Industrial solid waste comprehensive utilization rate	The percentage of comprehensive utilization of industrial solid waste in the production of industrial solid waste (%)	Response	[73]	
LCEQ2: Urban household garbage harmless disposal rate	The ratio of the amount of garbage treated by harmless treatment to the total amount of garbage treated (%)	Response	[74]	
LCEQ3: Industrial sulfur dioxide emissions	The amount of sulfur dioxide released into the atmosphere by enterprises during fuel combustion and production processes (t)	Response	[75]	
LCEQ4: Days with good air quality in the city	The number of days when the concentration of pollutants in the city's air is below the standard value (day)	Response	[76]	
LCEQ5: Greenery coverage of urban area	Percentage of urban built-up area covered by greenery (%)	Response	[77]	
LCEQ6: Green space per capita	The average area of public green space per resident in a city (m2)	Response	[78]	
LCEQ7: Sewage treatment rate	Proportion of treated domestic sewage and industrial waste water in total sewage discharge (%)	Response	[79]	
LCEQ8: Proportion of environmental protection investment	The proportion of investment in environmental pollution prevention and control, ecological environment protection and construction in GDP of that year (%)	Response	[80]	
Low-carbon management	
LCM1: Low-carbon demonstration
Project development score	Construction and completion of low-carbon demonstration projects	Response	[81]	
LCM2: Low-carbon policy perfection score	Publicity and subsidy policies for low-carbon construction	Status	[82]	
LCM3: Perfection of greenhouse gas statistical accounting assessment score	Monitoring and evaluation of greenhouse gases	Status	[83]	
LCM4: Reasonableness of urban planning score	Construction of urban industry and transportation	Status	[83]	
LCM5: Low-carbon technology perfection score	Low carbon talent introduction and low carbon technology innovation	Status	[84]	

3.3 MCDM-based evaluation the city low-carbon level

The MCDM method is a decision analysis technique used for selecting or evaluating a set of alternatives under multiple conflicting criteria. Through this method, decision-makers can consider a variety of different evaluation standards and indicators comprehensively, aiming to achieve the best balance among multiple objectives. The MCDM method is widely applied in fields such as economic management, environmental protection, and engineering design, assisting decision-makers in making more scientific and rational choices in complex decision-making environments. In this study, the MCDM method is utilized to evaluate the smartness and LCL of sample cities.Step 1 Establishment of weighting values between indicators by AHP method.

The Analytic Hierarchy Process (AHP) was proposed by the American operations researcher Saaty [52]. It quantitatively evaluates the importance between hierarchies by judging objective laws. The method scores the relative importance between indicators through pairwise comparisons, then establishes multiple judgment matrices to obtain the weight values of each indicator. This method is widely applied in most MCDM methods to determine the weights of indicators. In constructing the pairwise comparison matrices, experts scored different factors based on their expertise, as indicated in Table 2. The specific calculation steps are outlined in equation (1) through 3 [52].(1) D=[1p1p2⋯p1pjp2p11⋯p2pj⋮pjp1⋮pjp2⋱⋯⋮1]

(2) T=(d1,d2,⋯,dj)

(3) W=(w1,w2,⋯,wj)=(d1∑j=1ndj,d2∑j=1ndj,⋯,dj∑j=1ndj)

where D is the judgment matrix constructed based on the scoring value vector P=(p1,p2,⋯,pj)T of the indicators, T is the eigenvector of the judgment matrix, and W is the weight vector of the indicators.Step 2 Evaluation of LCL by TOPSIS method.

TOPSIS method is a widely used approach for comprehensive evaluation within a group. It ranks evaluation objects based on their proximity to an ideal goal and assesses their merits [53]. In this study, the evaluation is performed by calculating the distance between each evaluation object and the best and worst scenarios [54]. The closeness coefficient (Ci) represents the degree of proximity between each evaluation object and the worst scenario in this paper. A higher value of closeness indicates a better LCL in a sample city [54]. The parameters COverall, CLCE, CLCS, CLCEQ, and CLCM are selected to represent the closeness in terms of overall-LCL, LCE, LCS, LCEQ, and LCM. The specific calculation steps are outlined in equation (4) through 7 [55].(4) V=(ω1P11ω1P21ω2P12ω2P22⋯…ωmP1nωmP2n⋮⋮⋱⋮ω1Pm1ω2Pm2⋯ωmPmn)

(5) {V+={maxjVij|i=1,2,…,m}V−={minjVij|i=1,2,…,m}

(6) {Li+=∑j=1n(Vij−Vj+)2Li−=∑j=1n(Vij−Vj−)2

(7) Ci=Li−/(Li++Li−)

where V+ and V− respectively represent the best ideal solution and the worst ideal solution, Li+ and Li− represent the distances from the objective to the positive and negative ideal solutions, respectively. Ci indicates the closeness of the evaluation objective to the optimal solution, with Ci∈[0,1],i=1,2,…,m. A larger Ci value suggests stronger low-carbon level of the sample city.Step 3 Correlation analysis between smartness and low-carbon level of sample cities.

The Pearson correlation method is commonly used to measure the correlation coefficient between two continuous random variables, thereby assessing the degree of correlation between them [56]. In this study, based on the results from Steps 1–3, two sets of data are obtained representing SCP and LCL of sample cities, A:{A1,A2,…,An} and B:{B1,B2,…,Bn}. The overall means and covariance of both data sets are calculated, resulting in the Pearson correlation coefficient between the two variables. The specific calculation steps are outlined in equation (8) through 9 [57].(8) {E(A)=∑i=1nAinE(B)=∑i=1nBin

(9) ρAB=cov(A,B)σAσB=∑i=1n(Ai−E(A))(Bi−E(B))∑i=1n(Ai−E(A))2∑i=1n(Bi−E(B))2

where Ai and Bi respectively represent the SCP and LCL of sample cities. E(A) and E(B) are the overall means of the two data sets, σAandσB are their respective standard deviations, cov(A,B) is the covariance, and ρAB is the Pearson correlation coefficient. And the correlation coefficient approaches 0, the relationship weakens, as it nears −1 or +1, the correlation strengthens.

Based on the aforementioned Pearson analysis method to evaluate the correlation between smartness and LCL in the sample cities, if a correlation between the two variables exists, then a linear regression analysis is conducted to determine the existence and strength of the relationship between smartness and LCL. In the linear regression model, the closeness coefficients representing the low-carbon level score (COverall, CLCE, CLCSP, CLCEQ, and CLCM) are used as dependent variables, while urban smartness adopted from national reports is used as the independent variable. Moreover, the P-value helps to determine the significance of the results. A small P-value (typically ≤0.05) indicates that the regression equation established is statistically significant, meaning there is a linear relationship between the independent and dependent variables. R-squared is a statistical measure of how close the data are to the fitted regression line, representing the percentage of response variable variation explained by a linear model. Generally speaking, the higher the R-squared, the better the model fits the data.

4 Empirical results of China

4.1 Selection of sample cities in China

The smartness data of the sample cities can be sourced from the smart cities assessment report (CCID, 2023). This report employs a comprehensive evaluation method to assess 100 smart pilot cities in China, assigning an overall score of 100 and an average score of 68.92. However, due to the nascent stage of low-carbon construction in many Chinese cities, data pertaining to low-carbon city development is limited. To ensure the availability and validity of data, this study selected case cities from three batches of smart pilot cities announced by the Chinese government in 2013–2014, as well as three batches of low-carbon pilot cities announced from 2010 to 2017. The selected case cities must demonstrate a commendable level of both smart and low-carbon construction, with their respective government bodies releasing explicit construction data. Ultimately, considering factors such as economic development and geographical location, 36 smart cities were chosen as sample cities for analyzing the level of low-carbon construction based on the CCID report. These sample cities encompass 4 municipalities, 15 sub-provincial cities, and 17 non-sub-provincial capital cities. It's worth noting that these cities are predominantly China's capital cities and span all economic belts, ensuring the representativeness and validity of the assessment data.

4.2 Selection of valid indicators and data collection

Based on the DPSIR model, the conceptual framework for this research comprises four dimensions: low-carbon society, economic, environment quality, and management. These dimensions serve as the basis for indicator selection. The final-LCL indicators for cities are determined after expert grading, as depicted in Table 3.

These sample smart cities in China are both low-carbon pilot cities, with ongoing low-carbon construction efforts since 2010. Data on low-carbon society, economic, and environmental quality can be sourced from the China City Statistical Yearbook 2023. The low-carbon management indicators listed in Table 3 for the 36 sample smart cities can be obtained through qualitative scoring, as outlined in Appendix A-Table A1. Each indicator is awarded a scoring point based on the presence of corresponding scoring activities.

4.3 Weighting values between evaluation indicators

As per Section 3.3.2, the AHP method and expert scoring method were employed to determine the final weighting results for the four evaluation dimensions and 30 indicators. Table 4 displays the scores, with the low-carbon economic index receiving the highest score, indicating its significance, followed by low-carbon society. Notably, the five indicators with the highest weights are LCM4, LCM5, LCE4, LCE2, and LCM2, while the five indicators with the lowest weights are LCEQ1, LCS3, LCEQ7, LCS5, and LCE5.Table 4 Weighting values between indicators.

Table 4LCE	LCE1	LCE2	LCE3	LCE4	LCE5	LCE6	LCE7	LCE8	LCE9	
ω1	0.2965	0.0233	0.0484	0.0463	0.0512	0.0182	0.0253	0.0315	0.0237	0.0286	
LCS	LCS1	LCS2	LCS3	LCS4	LCS5	LCS6	LCS7	LCS8		
ω2	0.2448	0.0449	0.0284	0.0137	0.0463	0.0168	0.0269	0.0312	0.0366		
LCEQ	LCEQ1	LCEQ2	LCEQ3	LCEQ4	LCEQ5	LCEQ6	LCEQ7	LCEQ8		
ω3	0.2243	0.0121	0.0053	0.0282	0.0331	0.0460	0.0453	0.0162	0.0381		
LCM	LCM1	LCM2	LCM3	LCM4	LCM5					
ω4	0.2344	0.0349	0.0475	0.0363	0.0584	0.0573					

4.4 Evaluation results of the LCL between sample cities

4.4.1 Temporal characteristics analysis of the comprehensive evaluation results

(1) Characteristics of temporal changes in the comprehensive evaluation results. Based on the comprehensive evaluation results of subsystems and Equations (4), (5), (6), (7), (8), (9), as shown in Appendix-Table A3 and Fig. 2F.

From a broad perspective, the evaluation of LCL in the sample smart cities was conducted using the TOPSIS method, which yielded five closeness coefficients (COverall, CLCS, CLCEQ, and CLCM) as detailed in Appendix A-Table A3. The smartness level rankings of the sample cities are also provided in Appendix A-Table A3. The overall LCL indicators are divided into five grades based on a range of secondary indicators: lowest (0–2), low (2–2.5), moderate (2.5–3), high (3–4), and highest (4–5). The distribution of sample cities across these categories is illustrated in Appendix A-Table A2, with 11.11 % classified as high, 36.11 % as moderate, 47.22 % as low, and 5.56 % as the lowest in LCL. To effectively visualize the data, Fig. 2 depicts the spatial distribution of LCLs and individual dimension levels for the 36 sample cities. High-LCL cities are marked in red, the lowest-LCL cities in dark green, low-LCL cities in light green, and moderate-LCL cities in yellow. Cities not included in the study are shown in white. Furthermore, the average overall LCL (closeness coefficient) among the 36 sample cities is 2.5272, with a range from zero to five. Although these cities are representative of China's economic and political status, it is clear that the overall LCL of smart cities in China is still developing. Beijing has the highest LCL at 3.3328, while Lhasa in Tibet Province has the lowest at 1.4405, indicating a nearly twofold difference and significant variations among the sample cities.Fig. 2 Spatial distribution of low-carbon levels in each dimensions for sample smart cities: A) LCE, B) LCS, C) LCEQ, D) LCM, E) Overall-LCL, F) Overall smartness.

Fig. 2

Fig. 2E shows that the LCLs of Chinese cities in the eastern coastal region are significantly better than those in the western and northern regions. Among the sample cities, Beijing, Shenzhen, Guangzhou, and Shanghai exhibit high levels of both LCL and smartness. Beijing is China's political center, Shanghai its economic hub, Shenzhen the fastest-growing city, and Guangzhou the largest economic province in Guangdong. These cities are globally recognized for their economy, technology, and governance, with Beijing and Shanghai ranked among the top 10 cities worldwide, and Shenzhen and Guangzhou among the top 50. In 2020, the Chinese government launched an action plan to establish low-carbon pioneer cities, expecting these four cities to lead in achieving carbon neutrality. Conversely, Lhasa in Tibet and Urumqi in Xinjiang Province have the lowest-LCL. These areas experience slower economic growth compared to the aforementioned cities and are located on the Qinghai-Tibet Plateau, characterized by vast territories and small populations. The prevalent traditional and highly polluting economic development mode contrasts with their early-stage low-carbon management systems. However, Lhasa and Urumqi have abundant clean energy resources, such as solar, water, and wind energy, highlighting their potential for low-carbon development. Tibet and Xinjiang are key to China's strategy to transmit electricity from the west to the east, making the development and utilization of clean energy in these regions strategically important for achieving China's carbon emissions peak by 2030.(2) Characteristics of temporal changes of the comprehensive evaluation results in each dimensions.

Based on the comprehensive evaluation results of subsystems and Equations (4), (5), (6), (7), (8), (9), as shown in Appendix-Table A3 and Fig. 2A–D.

From the perspective of the economic, society, management, and environmental quality subsystems of low-carbon cities, Fig. 2A to D reveal significant variations in the four dimensions of LCL across different cities. In terms of low-carbon economy, society, and management (as shown in Fig. 2A, B, and 2D), Beijing, Shanghai, Guangzhou, and Shenzhen are ranked among the top four smart cities. These cities have achieved considerable progress in transitioning from traditional economic development models to newer ones, such as the information economy, knowledge economy, and innovation economy. As representative cities in China's economy and politics, they began the construction of a greener China in 2012, accumulating valuable experience for the low-carbon development of other cities. Furthermore, the spatial distribution of low-carbon environmental quality, as depicted in Fig. 2C, reveals an intriguing phenomenon: cities with larger economies and populations tend to have lower levels of low-carbon environmental quality. For example, Beijing, the political and economic hub of China, ranks seventh with a low-carbon environmental quality value of 0.708. This unsatisfactory outcome is largely due to the challenges of rapid urbanization, including traffic congestion, environmental degradation, and resource scarcity, which are inherent in the urbanization process. In response, the Chinese government has investigated various strategies to mitigate these issues. Among these, transitioning from a single-center urban development model to a multi-center development approach has proven to be the most effective.

4.4.2 Correlation results between smartness and low-carbon levels for sample cities

The correlation coefficient reveals the strength of the relationship between urban smartness and low-carbon levels. The Pearson correlation analysis results for smartness, overall-LCL, LCM, LCE, LCEQ, and LCS are presented in Table 5. This analysis identified a positive correlation between these variables. Specifically, the correlations between smartness and overall-LCL, LCE, LCS, and LCM are strong, whereas the correlation with LCEQ is weak.Table 5 Person coefficient about smartness and low-carbon levels in each dimension.

Table 5		Smartness	Overall-LCL	LCE	LCS	LCEQ	LCM	
Smartness	Person coefficient	1	0.877a	0.756a	0.882a	0.496a	0.814a	
Overall-LCL	Person coefficient	0.877a	1	0.896a	0.938a	0.649a	0.911a	
LCE	Person coefficient	0.756a	0.896a	1	0.851a	0.493a	0.704a	
LCS	Person coefficient	0.882a	0.938a	0.851a	1	0.521a	0.808a	
LCEQ	Person coefficient	0.496a	0.649a	0.493a	0.521a	1	0.459a	
LCM	Person coefficient	0.814a	0.911a	704a	0.808a	0.459a	1	
a The correlation is significant when the confidence level (double test) is 0.01.

In detail, the relationships between smartness, overall-LCL, and low-carbon construction levels across five dimensions were examined using linear regression analysis, with results displayed in Fig. 3. It presents the outcomes of this analysis, highlighting the correlation between smartness and the LCL of the 36 sampled cities. There is a notable correlation between the overall-LCL and smartness, as depicted in Fig. 3A (R2 = 0.769, P < 0.001). However, the strength of the correlation varies across the different low-carbon dimensions (economic, society, environmental quality, and management). Fig. 3B, C, and 3E show a significant correlation between smartness and the economic, society, and management aspects of low-carbon development (R2 = 0.572, R2 = 0.778, R2 = 0.663, P < 0.001), respectively. In contrast, as Fig. 3D illustrates, the correlation between smartness and low-carbon environmental quality is not significant. Given that urban low-carbon environmental quality indicators derive from objective data related to economic development, population changes, and natural conditions, their direct connection to smart city construction remains unclear. Further research is required to ascertain whether specific smart city construction measures can improve low-carbon environmental quality.Fig. 3 The scatter and regression of smartness and low-carbon levels in each dimension: A) Smartness & Overall-LCL, B) Smartness & LCE, C) Smartness & LCS, D) Smartness & LCEQ, and E) Smartness & LCM.

Fig. 3

5 Discussions and implications

5.1 Relationship between smartness and low-carbon level of different cities

Although empirical data results have shown at a macro level that smartness significantly positively influences urban low-carbon levels, at a micro level, there are significant differences in the underlying mechanisms between smart cities and low-carbon cities, such as construction goals, construction subsystems, and construction elements. From a macro perspective, the construction of smart and low-carbon cities in China is based on the “Five-in-One” development concept (i.e., the coordinated development of economy, politics, culture, society, and ecological civilization), aiming to achieve sustainable urban development goals. In recent years, as the concept of people-oriented development continues to spread within urban development, aspects such as the sense of fulfillment in smart cities and the participation level in low-carbon cities have increasingly focused on respecting and paying attention to the feelings of urban residents. These development goals align closely with the United Nations’ Sustainable Development Goals for cities (SDG11 SDG13). However, from the perspective of specific construction indicators, smart cities emphasize the integration of emerging information technologies with comprehensive urban services, such as smart transportation and smart governance. In contrast, low-carbon cities focus more on developing low-carbon economies, societies, ecologies, management, and cultures from the perspective of complex urban systems. This difference in development focus at the micro level leads to significant variations in the paths of smart and low-carbon development across cities, as shown in Fig. 4.Fig. 4 Comparative analysis of smart and low-carbon city construction concepts and approaches at the macro-level.

Fig. 4

As shown in Fig. 3A, only four cities (Beijing, Shanghai, Guangzhou, and Shenzhen) have both high levels of smartness and low-carbon development. These cities are predominantly located in coastal areas of Eastern China, such as the Yangtze River Delta, and serve as either economic or political centers of China. They boast advanced technological capabilities and abundant material and human resources, making them among the first in China to integrate smart and low-carbon development into urban planning. Drawing on the experiences of developed countries like Singapore, the United States, and the United Kingdom, these cities have, after decades of model innovation, institutional reform, and technological advancement, established comprehensive smart infrastructure systems and made significant strides in industrial restructuring.

However, the majority of Chinese cities exhibit relatively low levels of smartness and low-carbon development, reflecting the challenges in urban development across the country. Despite China hosting the largest number of smart and low-carbon city pilots globally, there remains a substantial gap between China and developed countries in terms of smart and low-carbon city development. While China has a significant scale of internet and computing infrastructure, issues such as the lack of integrated and intensive infrastructure operation, aging infrastructure, and low levels of intelligence are severe. In terms of low-carbon city development, most Chinese cities still rely on traditional, extensive economic growth models. The economic “growing pains” associated with transitioning to a green, low-carbon industrial structure cause many cities to hesitate. Very few cities, as presented in Fig. 3A, exhibit characteristics of low smartness but high low-carbon levels, such as Xining, Kunming, Lhasa, and Yinchuan. These cities possess abundant forest carbon sinks and clean energy sources like wind and solar energy, providing them with a natural advantage in transitioning to a green economic structure. Leveraging big data and Internet of Things (IoT) technology, combined with green financial mechanisms like carbon credits, represents an important direction for their sustainable economic development.

5.2 Relationship between smartness and low-carbon level in each dimension

Economics play a key role in supporting cities to adopt green technologies, improve public transportation, and promote efficient use of resources. As shown in Fig. 3B, the connection between low-carbon economy and smartness is relevant but not particularly significant. The initial stages of green economy transformation experience economic downturns and resistance to change in patterns, resulting in a weak correlation between smart city and low-carbon city construction. However, cities typically go through a “painful period” during the low-carbon transition of their economies. The digital economy, as a crucial measure for building a low-carbon industrial system in cities, can enhance the production efficiency of traditional industries. By reducing the use of raw materials and energy, it lowers the carbon footprint, achieves optimal resource allocation and management, and reduces over-exploitation and consumption of resources. All these efforts lead to economic growth in cities while minimizing environmental impact. Currently, governments across China consider digital technology an important measure for the economic green low-carbon transition. For example, Shanghai has explicitly included “the proportion of the digital economy in GDP” as a critical assessment indicator for low-carbon city construction in its 14th Five-Year Plan for urban development. Ningbo has indicated in its 2024 city work conference that it plans to lead with the digital economy to build an efficient and low-carbon modern industrial system.

In terms of society and management, based on a high R2 value, as shown in Fig. 3C and E, the connections between urban smartness and low-carbon levels are both present an opposite situation. The construction of smart cities aligns with the increasing demand of urban residents for safe and convenient lifestyles in the era of big data. Smart measures, such as smart security and smart traffic, meet people's needs and enhance the happiness of urban residents. Certain construction indexes in smart city development, like urban gas penetration rate and urban transportation, also align with low-carbon city construction indexes. Moreover, digital technologies significantly improve the efficiency of urban low-carbon management. Government departments, by integrating digital technologies such as big data analysis, the Internet of Things (IoT), and intelligent algorithms, can more accurately monitor and analyze data on urban energy consumption, traffic flow, etc., and develop precise and effective carbon reduction strategies through visualization tools and models. The Chinese government has made numerous attempts to enhance low-carbon management efficiency through digital technologies. For instance, Qingdao has established a low-carbon development basic data platform, which centralizes storage, management, and display of basic data related to greenhouse gas emission inventory compilation, covering significant energy-consuming enterprises, carbon verification enterprises, and relevant data on the economy, society, energy, etc., of the entire city and its districts.

On the contrary, the link between smart cities and low-carbon environmental quality, as illustrated in Fig. 3D, is weak. The development of a low-carbon environment is heavily influenced by natural conditions and requires significant time to improve. Consequently, the relationship between smart cities and low-carbon environmental quality shows a certain delay and is somewhat indirect. Additionally, in the 1990s, a period of rapid economic expansion in China, the combination of a complex population structure and a generally low educational level resulted in a widespread lack of awareness about low-carbon practices, causing considerable harm to the urban ecological environment. As a result, the Chinese government has made enhancing public awareness of low-carbon practices a crucial strategy for improving the urban ecological environment. Employing digital means to promote low-carbon principles and encouraging citizens to embrace green, low-carbon lifestyles and consumption models are currently focal points of the Chinese government's efforts. For instance, Guangzhou has developed a city-wide carbon inclusivity promotion platform that not only enhances citizens' awareness of low-carbon actions through access to low-carbon behavior data, real-time carbon saving calculations, and incentive redemptions. Beijing has used a carbon trading platform to encourage the public's interest in green travel, having served 1.76 million carbon-inclusive users and reducing carbon emissions by more than 180,000 tons to date.

In conclusion, this study posits that cities, as complex systems where economic, cultural, ecological, and social factors interact, can achieve a “dual improvement” in production efficiency and carbon efficiency while reducing energy and resource consumption. This is accomplished through the deep integration of digital technologies with various urban sectors, marking an important direction for the future development of smart and low-carbon cities in China.

5.3 Implications to implement policies for urban smartness and low-carbon development

Digital technologies significantly empower carbon neutrality, playing a pivotal role in addressing climate change globally. Research by the International Telecommunication Union (ITU) suggests that digital technologies have the potential to reduce worldwide carbon emissions by 15 %–40 %. This reduction is achieved through streamlining activities, enhancing the efficiency of economic operations, and fostering a shift towards non-material forms of consumption. Globally, nations have recognized the value of digitalization in achieving their decarbonization goals. In the United States, a comprehensive approach towards digital decarbonization is evident through the adoption of various policy measures aimed at stimulating technological innovation and the development of new tools. The European Union has taken strides in leading the digital and green transition by establishing strategic frameworks that guide the dual transformation efforts. Japan has focused on integrating policies that not only foster digital growth but also enhance green development, demonstrating the effectiveness of policy synergy in promoting sustainable advancement.

This study underscores the significance of integrating smartness and a low-carbon ethos as foundational pillars for the sustainable development of modern cities. The relationship between urban low-carbon initiatives and digital advancements ought to be one of synergy and complementarity, rather than being pursued in isolation. A paradigm shift is proposed, advocating for a smart low-carbon co-development model. This model positions “low-carbon” efforts as the cornerstone of sustainable urban development, with “smartness” providing the technological backbone necessary for realizing these ambitions.

However, China's journey towards establishing low-carbon cities is currently navigating the initial stages of policy formulation. There is a notable absence of precise standards for industrial carbon emissions and a lack of robust measures for carbon management. To bridge this gap, it's imperative that targeted standards and regulations are developed for each industry, reflecting their unique operational realities and environmental impact. Moreover, the establishment of an inclusive governance framework that encourages active participation from various stakeholders, such as government, businesses, and citizens, is essential. Such a collaborative approach enriches the decision-making process and heightens the effectiveness of urban governance. It fosters a sense of inclusion and belonging among diverse social groups, contributing to social cohesion and stability. Adopting a bespoke approach to smart low-carbon development is essential, recognizing the unique characteristics and needs of different cities. Cities vary widely in terms of resource availability, economic structure, and demographic composition. Tailored strategies that consider these factors can spur regions with advanced economies to lead by example, promoting continuous technological innovation. Areas endowed with renewable resources, such as wind and solar energy, are poised to spearhead the green economy, leveraging digital innovations to transform their economic landscape. Particularly in China's western regions, cities like Lahsa and Urumqi face economic challenges but are rich in clean energy resources. Despite the slower pace of smart infrastructure development, the abundant availability of solar, water, and wind energy presents a unique opportunity. By harnessing these resources and investing in advanced ICT low-carbon technologies, such as intelligent photovoltaic systems, these cities can pioneer a new model of smart low-carbon development. This not only yields economic advantages but also positions them as leaders in the transition towards a more sustainable and low-carbon future.

6 Conclusion

As the global urbanization process unfolds, bringing economic growth and social development opportunities, it simultaneously introduces challenges such as environmental stress and resource limitations. The evaluation of urban smartness and low-carbon levels serves as a bridge linking the formulation of policies for urban resource and environmental management with the aim of fulfilling Sustainable Development Goals (SDGs 11.4, 11.6, and 11.b) at the urban level. Currently, the relationship between urban smartness and low-carbon levels lacks a consensus. This research introduces a method that merges qualitative and quantitative analysis, adopting a perspective of urban complex systems to explore the correlation between urban smartness and low-carbon levels. This novel approach reveals a strong positive correlation between urban smartness and overall low-carbon levels. Key insights from this innovative method include: (i) The ultimate objective of both smart and low-carbon cities is to achieve sustainable urban development, with an increasing emphasis on the “human-centric concept” in recent years; (ii) The correlation between urban smartness and low-carbon levels is subject to the interplay of diverse factors such as economic, social, cultural, political, and ecological influences.

This research selects 36 typical smart low-carbon pilot cities in China for analysis to explore the correlation between urban smartness and low-carbon levels. The key findings from this study are summarized as follows: (i) There is a high correlation between urban smartness and low-carbon levels, yet there are clear differences among cities, with cities in coastal areas significantly outperforming those in the northwest region; (ii) The relationship between urban smartness and city low-carbon levels is affected by a mix of economic, social, political, cultural, and ecological factors. Social and managerial factors have the strongest impact, economic factors have a moderate impact, and ecological factors have the weakest impact; (iii) Digital technologies can effectively improve the low-carbon levels of cities. For example, the construction of urban low-carbon management platforms can significantly enhance the efficiency of urban low-carbon management, and the establishment of intelligent digital infrastructure can effectively encourage low-carbon cultural practices in society.

The innovation of this study lies in overcoming the difficulties associated with capturing all types of carbon emissions due to the limitations of current measurement and estimation technologies. This challenge makes it hard to obtain comprehensive, accurate, and timely data on urban-level carbon emissions, complicating the assessment of urban low-carbon levels. Additionally, this research bridges the existing gap in understanding the correlation between urban smartness and low-carbon levels by employing both qualitative and quantitative analyses. From the perspective of urban complex systems, this study breaks down the urban low-carbon level into components such as LCS, LCM, LCE, and LCEQ, examining their correlations with urban smartness. This approach clarifies the mechanisms of influence between urban smartness and low-carbon levels, laying a theoretical groundwork for the co-development of smart and low-carbon cities. The study's limitations are also recognized. Firstly, it only includes a subset of Chinese cities in its sample analysis, which means the study's conclusions may not be fully representative. Secondly, the constructed set of indicators is still not comprehensive enough, leading to potential inaccuracies in the evaluation outcomes. Therefore, it is suggested that future research should extend the comparative analysis of the correlation between urban smartness and low-carbon levels across different cities, regions, and national contexts to enhance the guidance for urban sustainability practices.

Data availability statement

Data associated with the study has not been deposited into a publicly available repository. Data included in article/supplementary material/referenced in article.

CRediT authorship contribution statement

Xiongwei Zhu: Writing – original draft, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Dezhi Li: Supervision, Funding acquisition, Conceptualization. Shiyao Zhu: Writing – review & editing, Investigation. ShiAn Ting: Writing – review & editing, Data curation.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Dezhi Li reports financial support was provided by 10.13039/501100002949 Jiangsu Province . If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Table A1 Primary indicators for the evaluation.

Table A1No.	Primary Indictors	No.	Primary Indicators	
1	Low-carbon economic	2	Low-carbon society	
1.1	Industrial capacity utilization	2.1	Urbanization rate	
1.2	Industrial electricity consumption	2.2	R&D proportion	
1.3	Heavy industry proportion	2.3	Number of buses per 10,000 people	
1.4	Industrial carbon emissions	2.4	Urban road area per capita	
1.5	Energy Consumption per GDP	2.5	Traffic volume	
1.6	Per capital GDP	2.6	Engel coefficient	
1.7	Proportion of secondary industry in GDP	2.7	Population density	
1.8	Proportion of tertiary industry in GDP	2.8	Per capita consumption of electricity	
1.9	Per Capita Disposable Income of Urban	2.9	Gas penetration in cities	
1.10	Construction output value			
1.11	Proportion of high-tech industry added value in added value of industries above designated size in the whole city	3
3.1	Low-carbon environment quality
Transport carbon emissions	
4	Low-carbon management	3.2	Industrial solid waste comprehensive utilization rate	
4.1	Low carbon demonstration project development score	3.3	Urban household garbage harmless disposal rate	
4.2	Low-carbon policy perfection score	3.4	Industrial sulfur dioxide emissions	
4.3	Carbon emission industry standard perfection score	3.5	Days with good air quality in the city	
4.4	Perfection of greenhouse gas statistical accounting assessment score	3.6	Greenery coverage of urban area	
4.5	Low-carbon development investment score	3.7	Green space per capita	
4.6	Low-carbon technology perfection score	3.8	Sewage treatment rate	
4.7	Reasonableness of urban planning score	3.9	Proportion of environmental protection investment	

Table A2 Evaluation basis of low-carbon cities management dimension index.

Table A2Indicator	Scoring point	Source	
LCM1: Low-carbon demonstration project development score	S11 represents whether to build a low-carbon demonstration city	List of green ecological cities	
	S12 represents whether to build low-carbon industry	List of low-carbon industrial parks	
	S13 represents whether to build a low-carbon demonstration community	List of low-carbon community demonstration in each province	
LCM2: Low-carbon policy perfection score	S21 represents whether there are incentive policies (subsidies for new energy vehicles)	Low-carbon city work programme	
	S22 represents whether there are mandatory policies (low-carbon development plans or low-carbon regulations)	Low-carbon city work programme	
	S23 represents whether there is a voluntary policy(low-carbon publicity)	Low-carbon city work programme	
	S24 represents whether the carbon trading mechanism has been explored	Low-carbon city work programme	
LCM3: Perfection of greenhouse gas statistical accounting assessment score	S31 represents whether GHG monitoring and evaluation is being carried out S32 represents whether to establish an open platform for energy data network	Low-carbon city work programme
Low-carbon city work programme	
LCM4:Reasonableness of urban planning score	S41 represents whether there is industrial spatial agglomeration	Low-carbon city work programme	
	S42 represents whether there is rail transit	Fact-finding	
	S43 represents whether to carry out low-carbon construction from the perspective of urban planning	Low-carbon city work programme	
LCM5: Low-carbon technology perfection score	S51 represents whether to establish low-carbon technology innovation platform	Low-carbon city work programme	
	S52 represents whether to introduce low-carbon technical talents	Low-carbon city work programme	
	S53 represents whether to promote the use of low-carbon technology	Low-carbon city work programme	
*Evaluation indicators scoring rules.

Satisfying the scoring criteria earns one point, while failure to meet the criteria results in zero points. The cumulative score represents the final score for that dimension.

Table A3 Results of the comprehensive low-carbon index between 36 smart cities.

Table A3Smart Cities	Smartness	CLCE	Rank	CLCS	Rank	CLCEQ	Rank	CLCM	Rank	COverall	Overall Rank	
Beijing	87.13	0.9069	1	0.7179	1	0.708	7	1.0000	1	3.3328	1	
Tianjin	78.69	0.7183	8	0.5876	9	0.58	26	0.8742	6	2.7601	11	
Shijiazhuang	70.15	0.5181	33	0.3985	30	0.503	35	0.5606	34	1.9802	35	
Taiyuan	62.08	0.615	20	0.4014	29	0.494	36	0.6903	26	2.2007	31	
Ji'nan	76.44	0.6347	19	0.4925	19	0.577	27	0.7006	23	2.4048	21	
Huhehaote	62.42	0.6573	16	0.3433	35	0.695	12	0.3784	35	2.074	34	
Shenyang	76.15	0.5818	24	0.4837	20	0.577	27	0.6932	25	2.3357	24	
Dalian	74.35	0.6379	18	0.4635	22	0.641	22	0.6151	32	2.3575	22	
Changchun	68.52	0.5534	29	0.438	26	0.61	24	0.6897	27	2.2911	28	
Haerbin	67.20	0.5889	23	0.4131	28	0.615	23	0.7147	21	2.3317	25	
Shanghai	87.42	0.8987	2	0.6645	2	0.709	6	0.9577	2	3.2299	2	
Nanjing	78.74	0.7736	5	0.5859	10	0.67	15	0.8891	5	2.9186	6	
Hangzhou	85.2	0.7694	6	0.6252	6	0.686	14	0.8731	7	2.9537	5	
Ningbo	75.42	0.6381	17	0.5546	13	0.688	13	0.6983	24	2.579	15	
Hefei	73.38	0.5347	30	0.4795	21	0.572	29	0.7332	17	2.3194	27	
Fuzhou	76.28	0.5775	27	0.5317	16	0.714	5	0.6767	29	2.4999	18	
Xiamen	75.34	0.661	15	0.578	11	0.748	1	0.7853	13	2.7723	9	
Nanchang	70.29	0.531	31	0.4435	25	0.652	19	0.7236	19	2.3501	23	
Qingdao	81.07	0.6884	13	0.5252	17	0.703	10	0.8083	12	2.7249	12	
Zhengzhou	74.17	0.5937	22	0.5457	15	0.569	30	0.7453	15	2.4537	20	
Wuhan	77.95	0.6888	12	0.6117	7	0.646	20	0.8255	10	2.772	10	
Changsha	73.68	0.71	10	0.5515	14	0.646	20	0.7784	14	2.6859	13	
Guangzhou	86.49	0.8815	4	0.6532	4	0.705	9	0.8979	4	3.1376	4	
Shenzhen	89.36	0.8826	3	0.6638	3	0.736	2	0.9232	3	3.2056	3	
Nanning	66.04	0.5189	32	0.4246	27	0.661	17	0.7166	20	2.3211	26	
Haikou	60.52	0.6688	14	0.4588	23	0.706	8	0.7055	22	2.5391	17	
Chongqing	79.90	0.7083	11	0.6343	5	0.703	10	0.8313	9	2.8769	7	
Chengdu	75.09	0.714	9	0.5974	8	0.67	15	0.8617	8	2.8431	8	
Guiyang	73.93	0.5795	26	0.4526	24	0.718	4	0.7248	18	2.4749	19	
Kunming	68.98	0.5798	25	0.5035	18	0.722	3	0.7356	16	2.5409	16	
Xi'an	76.99	0.7236	7	0.5675	12	0.569	30	0.825	11	2.6851	14	
Lanzhou	65.52	0.6012	21	0.3876	31	0.554	32	0.6816	28	2.2244	29	
Xi'ning	60.67	0.5142	34	0.3538	34	0.661	17	0.6722	30	2.2012	30	
Yinchuan	67.71	0.4456	36	0.3631	33	0.605	25	0.6643	31	2.078	33	
Urumqi	52.31	0.5679	28	0.3744	32	0.552	33	0.5875	33	2.0818	32	
Lahsa	48.63	0.4717	35	0.3429	36	0.516	34	0.1099	36	1.4405	36	

Table A4 SCP evaluation index system.

Table A4No.	Evaluation indicators	No.	Evaluation indicators	
1	Smart public service (SPE)	2	Precise governance (PG)	
1.1	SPE1: Traffic Information Services	2.1	PG1: Grassroots Smart Governance	
1.2	SPE2: Online Social Security Services	2.2	PG2: Smart Emergency Management	
1.3	SPE3: Smart Medical Services	2.3	PG3: Smart Emergency Management	
1.4	SPE4: Online Education Services	3	Information infrastructure (II)	
1.5	SPE5: Employment Information Services	3.1	II1: Information Networks	
1.6	SPE6: Smart Elderly Care Services	3.2	II2: Spatial-Temporal Information Platforms	
1.7	SPE7: Accessible Information Services	3.3	II3: Government Facilities	
1.8	SPE8: Integrated Government Services	3.4	II4: Information Security Protection	
4	Digital economy (DE)	5	Innovative Development Environment (IDE)	
4.1	DE1: Proportion of Digital Economy in GDP	5.1	IDE1: SMC Construction and Management System	
6	SCP citizen satisfaction (SCS)	5.2	IDE2: SMC Sustainable Operation Mechanism	
6.1	SCS1: SCP Citizen Satisfaction Survey	5.3	IDE3: SMC Reform and Innovation Practices	
*Evaluation indicators scoring rules.

SPE1 = 0.5 × (Real-Time Signal Timing System Coverage Rate + Urban Parking Information Service Coverage Rate) × 100, SPE2 = (0.6 × Street Social Security Self-Service Opening Rate + 0.4 × Township Social Security Self-Service Opening Rate) × 100, SPE3 = (0.6 × Electronic Medical Record Universalization Rate + 0.4 × Resident Electronic Health Record Archiving Rate) × 100, SPE4 = Education Special Network Coverage Rate × 100, SPE5 = Employment Information Service Usage Rate × 100, SPE6 = (Number of Communities with Home-Based Elderly Care Service Platforms/Total Number of Communities in the Jurisdiction) × 100, SPE7 = Internet Accessibility Setting Rate × 100, SPE8 = 0.5 × (Single Portal Handling Rate + Single Window Service Rate) × 100, PG1 = (Community Data Comprehensive Collection Rate) × 100, PG2 = City Event Emergency Command System Efficiency Rate × 100, PG3 = Public Area Video Surveillance Networking Rate × 100, II1 = 0.5 × (Gigabit Fiber Network Coverage Rate + 5G Network Coverage Rate) × 100, II2 = Spatiotemporal Information Platform Completion Rate × 100, II3 = Government Smart Facility Construction Completion Rate × 100, II4 = Data Security Level × 100, IDE1, IDE2, IDE3 evaluate the organizational leadership, construction management, and sustainable operational system innovations in smart cities, primarily determined through government unit surveys, SCS1 assesses public perception of smart city construction impacts, determined through citizen surveys.

Acknowledgements

This study was supported by the Carbon Peak and Carbon Neutral Technology Innovation Funding of 10.13039/501100002949 Jiangsu Province (BM2022035 ), and Carbon Peak and Carbon Neutral Technology Innovation Funding of 10.13039/501100002949 Jiangsu Province (BE2022606 ).
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