Achievement of global climate stabilization goals requires reduction of carbon emissions in the industrial world to a degree which effectively requires all developed world cities to become zero carbon between 2030 and 2050 (Kennedy & Sgouridis 2011). Such cities will source all energy from renewable energy sources. The operational landscape of electricity, heating and cooling, water and transport systems is increasingly integrating data collection, storage, sharing and analysis to optimize operation and align multiple stakeholders in each infrastructural value chain towards common goals of efficiency and cost-effectiveness (Fleming 2017).
Today’s cities are the engines of the new data economy. The rise of energy trading systems, distributed energy sources, electric vehicles, on-demand transport, intelligent water management and responsive lighting are rapidly replacing the legacy infrastructure and service delivery models that served the cities of the twentieth century (Barns 2018). As a consequence, the millions of interactions and transactions that take place in cities on any given day — from volumes of energy used, movements of people, traffic, water and waste, social media interactions, emails, financial and retail transactions — are now generating huge volumes of data of increasing value to governments and businesses as they seek to apply data-driven methodologies to improve the quality and efficiency of city services (Ahmadi Zeleti et al. 2016).
Both academia and industry are focusing on energy management in cities, with many practical solutions developed at the level of buildings or individual city systems (Ejaz et al. 2017). Integrating these systems requires upgrading the institutional knowledge infrastructure associated with participation in decision making, public and social services, transparent governance, and political strategies and perspectives (Bibri 2018). Data science can empower a city with new insights and intelligence to support the identification of patterns and human relationships, enabling citizens, planners and city managers with smart instruments that support appropriate decision-making, discovery, exploration, and explanation on complex city dynamics (Kourtit et al. 2017).
Data science initiatives such as big data, internet of things, blockchain and business intelligence have the potential to improve implementation, foster greater understanding of social challenges, facilitate collaboration between governments, citizens and businesses, and usher in a new era of digital government services, but more research is needed to explore how best to consider data science in that context (Bertot & Choi 2013). In the energy sector, production and consumption data and energy systems are increasingly supported by emerging information technologies, changing the landscape of traditional energy industry where data science can lead to significantly improved efficiency and new business opportunities. Based on the big data analytics and services, energy is now being saved in ways that were not possible in the past (Zhou et al. 2016).
While governments have the unique legitimacy to collect and process large amounts of data generated within their borders and manage basic infrastructure to the benefit of all its citizens, private sector innovations based on technologies like big data, internet of things or blockchain cannot be replicated in the public sector with the same speed as they have in the private since their social impact is much greater (Kim et al. 2014). This may be due to difference in goals. The main goal of the private sector is to earn profits by providing goods and services, developing/sustaining a competitive edge, and satisfying customers and other stakeholders by providing value. In government, the main goal is to maintain domestic tranquillity, achieve sustainable development, secure citizens’ basic rights, and promote the general welfare and economic growth (Deloitte 2016). Most businesses aim to make short-term decisions with a limited number of actors in a competitive market environment. Decision making in government usually takes much longer and is conducted through consultation and mutual consent of a large number of diverse actors, including officials, interest groups, and ordinary citizens (Kim et al. 2014).
As an example of applied data science, big data could potentially transform city governments being more efficient, effective and evidence-based but critics point towards the limited capacity of government to overcome the siloed structure of data storage and manage the diverse stakeholders involved in setting up a data ecosystem (Giest 2017). In the past, city leadership had relatively limited engagement with users of city infrastructure, having to arrange specific public meetings or surveys to get feedback on system effectiveness and gather proposals for improvements and/or policy measures (Fleming 2017). Today and increasingly in the future, cities are able use the data from sensor networks embedded in city infrastructure and, through social media, people’s opinion to more rapidly identify opportunities to optimize all aspects of the city (Philip Chen & Zhang 2014).
In their effort to utilize big data, city governments largely focus on one of two data use models: a centralized structure where a city data centre is established or a decentralized model in which data scientists are integrated into different city departments who then compile data from various sources (Giest 2017). There is a concern that under time pressure, these structures are short term and end up enforcing the existing silos of people, IT and data. They further lead to an overemphasis on technological aspects of big data integration rather than more profound local government reform addressing the collection and use of big data. The focus on technology also pulls resources from a potential information management system that specifically addresses the ways in which the data are integrated into decision-making (Barns 2018). Adjusting government structure for energy strategy and policy implementation to accommodate data science elements increasingly challenges the existing capacities within the government and processes of evidence-based decision making.
As an example, using data that currently exists in most large cities, it is possible to estimate the annual carbon emissions at a city level, but not yet in real time or in terms of understanding the detail of citizens’ behaviours. There is a lack of both quality and quantity of data to provide a true city-wide picture in real time. Data are also not uniformly available, as data is rather concentrated in clusters (Kourtit et al. 2017). The challenge is acquiring these existing data, extracting useful information and transforming it into actionable knowledge to facilitate implementation of set policies and strategies. How to deal with the variations in availability and quality of data is a key issue for cities not just to address the digital divide but also for the research community to develop new algorithms to help close the gaps in data and knowledge (Creutzig et al. n.d.).
Image from: Frost & Sullivan