Urban Intelligence for Carbon Neutral Cities: Creating Synergy among Data, Analytics, and Climate Actions

Abstract

Cities are critical research subjects in carbon neutrality, considering they produce more than 70% of greenhouse gas emissions and their crucial role in taking climate actions. The pathway towards a greener society requires consensus, intelligence, and actions among global cities as a network of carbon neutral cities (CNC). Considering cities as complex system-of-systems, synergy among various sub-systems can create co-benefits through the progress towards carbon neutrality. Large volume, velocity, and variety of urban data provide new opportunities for quantifying, analyzing, and visualizing environmental–social–technical dynamics in urban systems. Rich data resources, advanced analytics, and climate actions collectively enable urban intelligence by leveraging data from heterogeneous sources with different spatial granularity and temporal frequency. Such intelligence can promote synergy across sub-systems and domains to support more responsive, precise, proactive planning, policy, and managerial actions. With a discussion on future innovation in urban intelligence for CNC, this paper presents conclusions on how urban intelligence can promote a smarter and greener society.

1. Introduction

Cities are a critical research context for climate actions due to their large-scale environmental effect, public attention, political influence, and economic impact [1,2]. Although cities only cover about 3% of the landmass, they produce about 72% of greenhouse gas emissions (GHGs) due to high density and intense production and consumption activities [3]. A key goal for climate actions is to achieve carbon neutrality, a state with net-zero carbon dioxide emissions generated by an individual, organization, enterprise, national, and even global scale [4]. Nowadays, leading cities worldwide have set up long-term plans to become carbon neutral cities (CNC) by upgrading infrastructure, improving management, optimizing operation, and transforming lifestyles towards greener urban living [5,6,7]. Meanwhile, the recent development of digital technology and smart city applications has rapidly transformed cities’ planning, operation, and governance [8,9]. Such transformation further enables urban intelligence, a core capacity to translate the high-level climate strategy into actionable insights with technological innovation and operational improvement [10,11,12].

Urban intelligence is a comprehensive capacity that integrates big data computing and information technology to improve the integration of urban systems and coordination across various domains [13]. In the research context of carbon neutrality, urban intelligence can generate synergy and maximize the co-benefits among different climate actions. However, the underlying value of urban intelligence has not yet been fully untapped due to a lack of conceptual framework on how such capacity may support cities through their pathway toward carbon neutrality [14]. Thus, a key goal of this study is to articulate the concept of urban intelligence and its critical role in carbon neutral cities.

In investigating above questions, this study proceeds with the following logic flow illustrated in Figure 1. First, an overview of global consensus on climate change and related high-level strategy on carbon neutrality provides a foundation for setting up the vision of carbon neutral cities. Second, viewing cities as complex system-of-systems provides a theoretical framework for understanding how various domains collectively contribute to urban carbon emissions. Third, the conceptualization of urban intelligence explains how a combination of data, analytics, and actions can promote the synergy and co-benefits among various climate actions. Finally, a discussion highlights several recent innovations and emerging concerns in urban intelligence, concluding on future research directions and efforts to empower global collaboration on urban intelligence for climate actions.

2. Carbon Neutral Cities

2.1. A Global Climate Consensus

Decisions and actions on carbon neutrality derive from global, national, regional, and city-level consensus on climate change. In the last 30 years, multiple treaties and agreements have formed a global climate consensus. The United Nations Framework Convention on Climate Change (UNFCCC) signed the first international environmental treaty on controlling GHG emissions in 1992 with more than 150 countries [15]. The Kyoto Protocol in 1997 is the first legally binding GHG reduction treaty to supplement UNFCCC with 183 countries [16]. Different countries have set up national strategies for more sustainable development throughout this period accordingly. For example, the United States National Congress passed the Global Change Research Act of 1990 as the strategic foundation for research investment in renewable energy and climate change [17]. In 2003, the United Kingdom released an energy white paper, “Our energy future—creating a low carbon economy”, and described the “low carbon economy” concept for the first time [18]. In 2015, 178 parties worldwide signed the Paris Agreement as the third international agreement to cope with climate change, setting a foundation for many countries’ most recent strategic plans since 2020 [19].

Most countries and regions now have developed long-term national strategic plans based on the Paris Agreement. National interests in carbon neutrality are multi-faceted for different countries, including improving the local environment and quality of life, easing the underlying risk in energy security with coal and gasoline, or transforming the economy through industrial upgrading. Therefore, each country establishes specific strategies based on its unique environmental, economic, technological, and social conditions. Major GHG emitters such as the United States, China, and the European Union have announced national or regional plans since the Paris Agreement. In December 2019, the European Union presented the European Green Deal as a roadmap towards more sustainable growth to achieve carbon neutrality by 2050 [20]. During the 75th United Nations Conference in 2020, China announced the national goal aiming for a carbon emission peak by 2030 and achieving carbon neutrality by 2060 [21]. Its national strategic plan identifies six major domains: energy, industry, transportation, buildings, carbon sink, and technology. After the United States re-joined the Paris Agreement, the Biden administration released a national plan in November 2021 to set up a long-term strategy including a 50–52% net GHG reduction of 2005 levels in 2030 and achieving net-zero greenhouse gas emissions by 2050 [22]. Other countries also released national plans based on the Paris Agreement’s vision. Japan, for example, announced the national strategic plan for carbon reduction in October 2020, which sets up the national goal of achieving carbon neutrality by 2050 and promoting a “green society” [23]. It further released a strategic plan for green society development to provide high-level guidance for transitioning Japan into a “carbon-zero society” [24]. According to this plan, a green and carbon-zero society will achieve more sustainable growth towards a “positive cycle of economic growth and environmental protection” with measures including decarbonization of electricity, development of hydrogen industry, and carbon recycling supported by robust digital infrastructure (e.g., smart grid, autonomous driving, smart home, and robotics) [25].

In addition to international and national efforts, there is increasing interest in forming a global network of cities to exchange experience in achieving carbon neutrality. Such consensus is derived from a concept of “global cities” that globalization has created a transnational network of most impactful cities with great economic power, cultural and political influence, technological advancement, research capacity, and other resources [26]. Global cities are active actors in shaping international relations and collaboration in climate actions since they usually have more resources and fewer obstacles than the national governments [27]. For example, C40 Cities (C40) is a global network of nearly 100 cities worldwide to form collaborations and experience-sharing to address climate issues [28]. This network was initiated in 2005 by the Mayor of London with another 18 megacities and now with 97 cities. The Carbon Neutral Cities Alliance (CNCA) is another collaborative network of leading global cities that aim to reduce GHG emissions by 80–100% by 2050 [29]. CNCA is a project by the Urban Sustainability Director Network and founded by multiple organizations, including Kresge Foundation, Rockefeller Brothers Fund, McKnight Foundation, and others [30]. In 2014, CNCA was initialized in Copenhagen, with 17 cities from 9 nations, including Australia, Canada, Denmark, Germany, Japan, Norway, Sweden, the USA, and the United Kingdom [31].

2.2. Carbon Neutrality in Cities

Carbon Neutral Cities (CNC) generate a net zero increase in emissions by reducing primary carbon emissions and offsetting residual emissions. According to Global Protocol for Community-Scale GHG Emission Inventories, a particular city’s GHG emissions can be estimated within multiple scopes [32,33]: Scope 1 refers to GHG emissions from sources within its geographic boundary; Scope 2 refers to emissions from the use of electricity, heating, and cooling within the city; Scope 3 refers to other emissions that occur outside of the city but as a result of activities within its geographic boundary.

Although different scopes vary based on local context, a recent study further describes general components within each scope: Scope 1 includes agriculture, forestry, other productive land use, industrial processes, product use, stationary fuel combustion, and in-boundary transportation. Scope 2 represents grid-supplied energy, connecting with stationary fuel usage and transportation. Scope 3 includes energy transmission and distribution (connected with grid-supplied energy in Scope 2), outbound transportation, outbound waste treatment (connected with in-bound waste), and other indirect emissions [5]. According to C40′s definition, a city will achieve carbon neutrality when it meets the following criteria during a given year, including [34,35]:

• Net-zero GHG emissions from fuel use in buildings, transportation, and industry (Scope 1);
• Net-zero GHG emissions from the use of grid-supplied energy (Scope 2);
• Net-zero GHG emissions from waste treatment (Scope 1 and 3);
• Net-zero GHG emissions from all other sectors if a city accounts for additional sectoral emissions within its GHG accounting boundary.

It is critical to clarify that carbon neutrality is not an ultimate phase but a temporary status, which constantly fluctuates with annual carbon emissions. Significant sources of urban carbon emissions include buildings, transportation, utilities, solid waste, and food [36]. Taking China as an example, its national annual total carbon emission of the whole building process was 4.93 billion tons, accounting for 51.3% of the national carbon emission in 2019 [37]. The national public building area was about 15.2 billion m², and the total energy consumption of public buildings (excluding northern heating) was 342 million tce (tons of standard coal equivalent). The increase in the total area of public buildings and the increase in energy demand have led to increased energy consumption per unit area of public buildings from 17 kgce/m² in 2001 to more than 26 kgce/m². In building operations, the energy consumption of the HVAC (heating, ventilation, and air conditioning) system and lighting system accounts for nearly 50~70%. Thus, retrofitting existing building stocks with better energy efficiency is a major approach to carbon emission reduction. Among all building systems, HVAC and lighting are at the forefront of achieving carbon neutrality goals for buildings [38].

2.3. Smart City Development

The smart city concept originated in 2008 as a part of IBM’s vision of the next-generation urban infrastructure, where power grids, food distribution networks, water, healthcare, and transportation systems are assisted by sensors and predictive analytics to create “intelligent systems” [39]. With a business focus on ICT infrastructure and software applications, IBM envisioned smarter cities that were instrumented with ubiquitous sensors, interconnected by various data, and that were intelligent due to insights from data analytics, modeling, and optimization. Besides the SaaS (software as a service) aspect of smart cities, government officials and real estate developers viewed smart cities as a futuristic prototype and development strategy for planning and constructing a new city or district, such as the Masdar City in the United Arab Emirates (initiated in 2006) and Songdo District in South Korea (initiated in 2008), representing an entirely new design practice, spatial configuration, management approach, and even new lifestyle for the residents [10,40].

Since 2010, ubiquitous computing has accelerated the exponential growth of information and led to the era of big data and open data movement in many countries, including the United States [41], the United Kingdom [42], Canada [43], and Singapore [44]. Such transformation makes urban data publicly available via online portals for greater government transparency and citizen participation. Meanwhile, cities realized that smart cities investment should not solely focus on systems improvement with sensors and automated control. Intelligence also includes scientific understanding and data-driven decision-making to improve the efficiency and equity of urban policy for a better quality of life [45]. With increasing international attention to climate change, smart city initiatives started to integrate more considerations into carbon neutrality by strengthening urban energy-sustainability nexus [46]. For example, the International Telecommunication Union (ITU) and the United Nations Economic Commission for Europe (UNECE), along with other international organizations, have proposed the United for Smart Sustainable Cities initiative (U4SSC) to achieve the United Nations Sustainable Development Goal 11 (SDG11) [47].

It is necessary to point out that smart cities do not always guarantee a green society. Although sustainability, climate resilience, and carbon neutrality are the core value of smart city development, unexpected impacts and adverse effects may arise if we deploy technology without proper regulation and well-thought planning. One example is the rapidly increasing volume of packaging waste from e-commerce and delivery services worldwide. Carbon reduction involving packaging waste relies on the more sustainable packaging material that can be easily degraded and improved with a smarter logistical and information system to transform the conventional relationship between the e-commerce sellers and consumers. Thus, the progress towards carbon neutrality requires incremental improvement in production efficiency, operation system optimization, or urban quality-of-life and requires pivotal transformation with new economic growth, governance approaches, and lifestyle changes. Due to such complexities, carbon neutrality should be considered a socio-technical status in which technical and social factors collectively drive the progress of carbon reduction and green society [48].

3. Pathway towards to CNC

Considering cities as agglomerations of human habitat with large-scale, intensive production and consumption activities, their pathway to carbon neutrality will be a long-term ecological, technical, and social process [49]. Since cities are system-of-systems (SoS), their transition into CNC is challenging with uncertainties and complexities. The SoS framework illustrates the relationship between CNC and a much larger scope of planetary health that involves internal, external, and intermediate effects (Figure 2). Based on this framework, a single city is a system of interest (SoI) as an open system with internal elements and external interfaces with external elements derived from its wider system of interest (WSoI), usually at the regional or national scale. The behavior and condition of WSoI partially depend on its configuration, and its internal mechanism relies on an even broader scope as its operating environment, usually at an international scale. Finally, the operating environment originated from a wider planetary and inter-planetary scale.

A city’s transit system, as an example, can be viewed as an open system but has a boundary with checkpoints and its management territory. In this way, a city has its internal transportation networks and regulatory mechanism. However, it is also well-connected with inbound and outbound flows outside of the city’s physical scope as a part of the regional or national highway network. Such a transportation system runs within a particular operating environment with various physical, environmental, and socioeconomic factors, such as the gas price change or incremental innovations in vehicle manufacture. Finally, some essential factors define a broader and fundamental environment for transportation, including basic physical and chemical laws defining the current engine mechanism and people’s demand for transit.

The pathway towards CNC involves actions for carbon reduction, carbon sink, and carbon offsetting across various systems, such as energy, food, waste, manufacturing, and natural conservation [50]. Direct carbon reduction relies on the energy mix change either by replacing emission-heavy supply with greener sources or reducing the demand [51]. Cities reduce direct emissions by improving energy efficiency, constructing net-zero buildings, replacing emission-intensive systems with greener energy, and cutting consumption-related emissions with a more sustainable lifestyle. In addition, carbon capture, utilization, and storage (CCUS) proactively prevent carbon dioxide emissions from entering the atmosphere. Some major CCUS approaches include capturing carbon from the source (e.g., power plants), reusing carbon as resources for production or services, or storing carbon in deep underground geological formations [52]. Carbon sink usually relies on more ecological approaches through natural or artificial reservoirs (e.g., vegetation and ocean systems) to contain, store, and absorb carbon-related compounds and lower the concentration of carbon dioxide in the atmosphere [53]. Carbon offsetting is a mechanism derived from the Kyoto Protocol that allows compensating carbon emission in one location by carbon reductions in another [54]. For industries (e.g., airlines and cargo) that face difficulty in reducing emissions internally, carbon offset serves as an alternative approach for having the equivalent emission reduction externally by offering financial payment for the use of renewable energy elsewhere [55].

Synergy is a key for aligning different carbon-related actions and achieving the holistic goal of CNC. In general, synergy generates the combined effects among multiple factors resulting in a larger overall impact than the sum of their separate effects, which is referred to as synergistic effects [56]. The word synergy originated from the Greek term synergos, which means “teamwork”, indicating the multi-party’s coordination and cooperation across systems. Synergy can mix and match various actions for climate adaptation and mitigation and bring compound effects with greater economic, environmental, and social benefits by aligning different actions and processes across multiple domains [57].

Table 1. Major carbon neutral approaches in cities and related synergy.

Carbon Neutral Approaches	Synergy Identified in Previous Studies
Emission Control	Air quality improvement with carbon emission reduction [61,62,63].
Transportation Planning	Reduced traffic volume with less emission and more non-vehicular travel, such as walking and biking, with associated health benefits [60,64,65].
Building Retrofitting	Improved energy efficiency with lower emission and financial cost and improved living quality with better indoor air quality, comfort, and safety [66,67,68,69].
Waste Management	Reduce carbon emissions and pollution caused by waste incineration and reuse waste as alternative resources (e.g., biomass energy) to create a circular economy [36,70,71,72,73,74,75].
Lifestyle Change	Encourage greener consumption and healthier lifestyle choices for carbon footprint reduction and promote public awareness for local climate actions [76,77,78,79].
Natural Conservation	Improvement of urban resilience, climate mitigation, ecological capacity, and carbon sink through urban forestry and greening improvement [80,81].

summarizes major approaches and synergy identified based on previous studies. For example, actions on carbon dioxide emission control can also reduce sulfur dioxide, nitrogen oxide, particulate matter 2.5, and other atmospheric pollutants, creating synergy between carbon and air pollution reduction with co-benefits in carbon neutrality, environmental quality, and public health effects [58]. A recent study in China indicated the synergy between the carbon neutrality goal, air quality goal, and the national public health plan may improve life expectancy growth by 0.88–2.80 years per person if the country achieves its national carbon neutrality goal in 2060 [59]. As another primary approach, transportation planning can promote synergy among better land use, traffic reduction, environmental impact, and active urban living with more pedestrian activity and non-vehicular travel behavior [60].

Considering cities’ complex biophysical, technical, and social factors, the pathway towards CNC requires synergistic thinking with a combination of linear problem solving (relational thinking), evolutionary scenarios (divergent thinking), and co-evolutionary synergies (emergent thinking) [51]. In reality, cities face technical limitations, policy hurdles, social conflicts, and organizational barriers. Thus, creating synergy in climate actions relies on scientific understandings of complex urban dynamics within a specific geographical, demographic, and cultural context and long-term partnership among stakeholders involving city government, public or non-profit agencies, private entities, community-based organizations, and the public. Since the development of smart cities and carbon neutral cities often progress concurrently, it is essential to discuss how information technology can further support intelligent systems, behaviors, and decision-making processes for supporting cities in achieving carbon neutrality.

4. Urban Intelligence for Carbon Neutral Cities

4.1. Composition of Urban Intelligence

Urban intelligence is a capability to gain contextual and situational awareness by perceiving information, analyzing data, generating knowledge, and directing responses, behaviors, and decisions for a better outcome in cities [11]. It represents a bundle of intelligence, including data intelligence, design intelligence, and crowd intelligence, with various social and technical factors [12]. Distinct from smart cities, urban intelligence emphasizes digitalization and analytical capacity based on old and new urban dynamics. With sensing infrastructure, information network management, and data analytics, urban intelligence derives from multi-dimensional layers of urban systems interconnecting the physical space, human society, and cyberspace. While previous studies investigated the multi-facet nature of urban intelligence from biological, physical, cultural, social, technical, and political perspectives, this study focuses on the “smart cities” aspect of urban intelligence. Narrowly speaking, urban intelligence emphasizes utilizing big data, the Internet of Things (IoT), ubiquitous computing, and artificial intelligence to empower complex operations and decision making in cities. Particularly for CNC, urban intelligence supports cities to achieve such synergy mainly through three aspects involving data, analytics, and actions (Figure 3).

4.1.1. Data

Data are the fundamental urban intelligence source that requires coordinated data disclosure, exchange, and integration efforts. Ubiquitous computing breaks data silos and enables a holistic understanding of carbon neutrality across different spatial scales and temporal frequencies. Data disclosure, integration, and visualization have significantly addressed the issue of information asymmetry between city government and citizens through multi-sectoral synergy and collective efforts. For example, localized micro-climate data are vital in energy performance assessment, especially for cities in tropical or cold areas [82]. Integrating such high-resolution data from multiple resources makes it possible to track emissions on a human scale, particularly in buildings, streets, and neighborhoods [83].

There are increasing efforts to promote open data and better urban data integration through policy and regulatory efforts. For example, the Europe Green Deal is a set of policies and agreements approved by the European Commission in 2020 to guide the future progress of climate neutrality by 2050 [84]. Among other initiatives, it emphasizes the importance of accessible and usable data as the core resource for innovation. Each country within the EU should value promoting better sharing and use of data to support future climate actions. In March 2021, the U.S. Department of Energy announced a USD 34.5 million R&D grant to support advanced climate research data science and computation tools [85]. The key goals of this grant include: (1) creating new computing methods to handle big data and diverse unstructured data related to climate change; (2) developing better analytical models for studying the complex process of carbon reduction; and (3) exploring intelligent decision-support systems utilizing machine learning and other intelligent technology.

4.1.2. Analytics

Analytics are indispensable for extracting actionable insights from data and developing more intelligent solutions empowered by advanced data computing and machine learning. Major analytics types include descriptive, diagnostic, predictive, and prescriptive analytics [86]. These four types of analytics aim to tackle different questions involving “what happened” (descriptive)—“why did this happen” (diagnostic)—“what is likely to happen” (predictive)—“what do we need to do” (prescriptive) [86,87]. In carbon neutrality, urban analytics can generate, support, and manage various synergies in intelligent urban systems configuration and operation through automation, behavior nudging, and optimization. Simulations models, decision optimization, and civic analytical tools demonstrate the unknowable impact of long-term actions and large-scale effects. For instance, a scenario-based simulation model can serve as a decision-supporting tool to assess how different policy scenarios may impact city-level CO₂ emissions [88].

Besides academic and scientific research, urban analytics is central in transforming data into actionable indicators and translating quantitative metrics into insights for guiding policy, planning, and operation. In 2021, the China National Information Center Smart City Development Research Center, Tencent Cloud, and Tencent Research Institute jointly issued the Modern City Physical Sign Evaluation System Research Report [89]. This report identifies six critical dimensions (infrastructure, economic development, ecological environment, cultural construction, people’s livelihood services, and governance) for monitoring and evaluating cities based on scientific understanding of the cities and information technology.

4.1.3. Actions

Actions are a final yet essential process with deployment and operation to address real-world problems with actions and tangible value through urban intelligence. Considering cities as large socio-technical systems (LSTS), implementation of CNC requires considering not just technical feasibility but also social complexities involving different parties and agendas. For example, transforming the legacy urban infrastructure into smart and carbon neutral ones faces both technical and social challenges, making such a transition not an easy and short-term process [90,91]. In general, urban intelligence needs to ensure proper implementation with socio-technical considerations, including but not limited to equity in access to technology, fairness of data-driven operation, inclusiveness of public participation, and safety involving information piracy and cyber security.

A key channel for deploying urban intelligence is urban planning, which integrates multifaceted aspects of cities’ physical, ecological, technical, social, economic, cultural, and political factors. In CNC, the primary goal of urban planning is to optimize the balance between carbon emission reduction and human well-being, which involves energy source replacement, infrastructure upgrading, waste management, and quality-of-life improvement with better design and operation [92]. Beyond academic research, urban planning also has a governmental function of identifying long-term goals and development strategy, making operational decisions, and executing policy and management at a national, regional, city, district, and community scale [93].

4.2. Roles of Urban Intelligence in Carbon Neutrality

4.2.1. Breaking Data Silos and Information Asymmetry

Urban intelligence can create better information management through data collection and integration to address information asymmetry and silos. Conventional urban information management often relies on a tree-shaped system with multiple administrative hierarchies, which is preferable for single-agency operations but not ideal for cross-sectoral coordination. As a result, valuable data are buried deep within a particular branch, constraining integration for greater regulatory and operational insights. Moreover, information asymmetry among city government, business operators, and residents as customers often creates obstacles for more synchronized actions to align the political agenda, market interest, and customer demands, collectively driving carbon emission reduction. One example is building stocks in cities where market hurdles and split incentives often constrain building renovation and retrofitting investment [94]. Regarding such issues, the author’s recent research investigated building construction permit information systems across seven major U.S. cities using natural language process techniques for unstructured data mining and information discovery [95,96]. Such novel data mining and integration can provide more insights into near real-time construction activities in cities with higher spatial granularity and temporal frequency.

4.2.2. Integrating Systems with Advanced Analytics

Urban intelligence utilizes advanced analytics to (1) enable more intelligent decision-making or operational processes and (2) gain a more holistic and systematic understanding of CNC across various domains. Data streams from IoT devices and cloud computing infrastructure enable real-time analytics for intelligent control systems and automation through the entire life cycle with better efficiency and accuracy but lower labor requests and time costs. Such intelligence can create synergy between planning, design, and operation for a single building system, such as an intelligent building control system for residential heating systems that can coordinate thermal comfort, cost reduction, and resultant carbon emissions [66]. In addition, intelligent systems can better integrate the construction, operation, maintenance, retrofit, and demolition by minimizing the embedded carbon emissions through the full life cycle of buildings [68]. On the other hand, waste treatment is a critical system in cities that requires further improvement with advanced analytics and artificial intelligence. For example, Greyparrot is a U.K.-based technology innovation company focusing on developing software for an AI-aided waste recognition system [97]. Besides analytics to support system control, the scientific understanding of CNC synergy also relies on integrated analysis beyond conventional sectors that focus on energy and utilities, integrating much broader aspects with urban design, ecological sink, and digital solutions. Such integrated analytical approaches can further estimate, quantify, and visualize the synergetic effects of multiple climate actions or scenarios in CNC.

4.2.3. Implementing Actions with Social-Technical Consideration

Urban intelligence can provide a systematic and holistic overview of the synergy involving carbon neutrality across various spatial–temporal scales and between human–nature–machine across different domains and parties. Besides better information systems and advanced analytics to support decisions and operations, cities also implement urban intelligence through innovation initiatives, pilot projects, and citizen science programs with participation from local communities.

City-level actions highly depend on specific social condition, demographic characteristics, and neighborhood demands, which require localizing global or national-level climate solutions based on their unique context [98]. Thus, a city may identify a specific neighborhood as a “testing-bed” and set up an exemplary best practice for smart and carbon neutral cities. For example, a recent study explored “connected intelligence” for net-zero energy districts (NZEDs) by synthesizing smart cities with renewable energy technology. Such intelligence integrates various capacities from individual human intelligence and group collective intelligence with smart technology implementations (machine intelligence) [99]. This research further proposed three major building blocks for intelligent systems integration: district characteristics, energy performance, and net-zero measures. Such implementations do not only provide a much more manageable physical scope (compared to the entire city) for CNC research and development (R&D) but also create opportunities for building trust between city government and citizens and long-term partnerships among multiple stakeholders. Eventually, urban intelligence can create synergy among implementations in environmental–capital–emission planning.

5. Discussion

Future urban intelligence can support CNC with more innovative digitization, analytics, and transdisciplinary collaboration. Internet of Things, ubiquitous computing, and big data enable better data collection representing the digital trace of complex processes in CNC. For example, digital twin technology can create a virtual representation of a physical product and its production, consumption, and recycling process, which enables more data-informed life-cycle management [100]. By integrating information systems, simulation models, and predictive analytics, digital twins can track and assess carbon emissions through product life cycle (e.g., lithium battery) and buildings [101,102]. In the future, we expect more applications utilizing digital twins for everyday consumer products so that we can monitor and optimize complex interactions across different logistics through material goods’ entire life cycle. Most current urban analytics focus on city government agencies or business operators for top-down data-driven decisions or policymaking. Considering lifestyle shift as one critical transformation for achieving CNC, future advanced analytics will provide more timely, intuitive, interactive, and user-friendly tools for general citizens to engage in such a synergistic process. In future, increasing analytical tools will make such synergy more measurable and tangible for general citizens. Cities will be able to positively affect consumer preference and choices with “behavior nudging” through the pathway of carbon neutral lifestyle transformation.

Synergy is not just an outcome product but a process that requires continuous synchronization and collaboration among different parties at multiple scales. We expect increasing research in urban intelligence among global cities to continue neutering collaboration, leadership, and consortium across different disciplines and domains of expertise. Such transdisciplinary research will promote more synergy among carbon emission reduction, economic development, public health, and social equity. Since urban intelligence deeply integrates data, analytics, and actions, we expect more emerging conflicts and controversies involving data ownership, algorithmic fairness, and digital governance between city agencies and local communities. Academic institutions will play a critical role in ensuring scientific soundness with social considerations. Furthermore, researchers in urban intelligence will balance data exchange with open-source technology with careful considerations of cyber security and data privacy.

6. Conclusions

Urban intelligence is the capacity to integrate data, analytics, and actions to support cities in making more informed decisions and responsive actions. In the specific context of carbon neutrality, urban intelligence promotes scientific understanding and synergy in carbon neutrality based on a system-of-systems view of cities. Meanwhile, future studies need to respond to ethical concerns involving data biases, algorithmic injustice, and underlying risks of urban intelligence, especially its unexpected impact on vulnerable communities. As smart city development and related information technology have an increasing impact on urban systems, more investigations need to identify urban intelligence’s core functions through the long-term ecological–technical–social transformation of becoming carbon neutral cities.