Exploring the role of big data analytics (BDA) in promoting smart low-carbon cities: A human-centered, community-based, and deep engagement approach in Hong Kong
Existing government-/power utility-led decarbonization measures will not be enough to deliver the rapid, deep decarbonization required to meet the 1.5℃ Paris Agreement target and avoid catastrophic climate impacts. Cities, as key arenas of sustainable urban transitions, are shifting their attention away from top-down (government-/power utility-led) approaches to engaging households and communities. More and more city governments have come to realize that getting citizens motivated and committed to sustained efforts is the key to building market demand and public support to delivering deep decarbonization. Rapid digitalization of energy systems, supported by tools such as smart meters and BDA, offer new possibilities for engaging households by building new social relationships (e.g. with family members, neighbours, local authorities, business sectors) within smart low-carbon societies. However, the development of smart low-carbon cities varies around the world, and outcomes have been mixed. Can households make a collective impact, as communities, in decarbonizing cities? How can this be achieved, in what circumstances, and with what results? These are questions which remain under-researched.
This interdisciplinary pilot project is the first phase of a two-phase project, utilizing a case community in Sai Kung with 30-40 households as a living site for experimentation and social learning with participatory engagement. The second phase will tentatively involve 600 households in four distinctive case communities in Hong Kong with the aim to expanding app-based BDA with tailored energy-saving advice. We aim to develop and test a model for enabling behavioral change among residential electricity consumers which is underpinned by both data science (smart sensors and BDA) and participatory engagement. The engagement “activity package” includes regular community activities (e.g. envisioning dialogues, installation art projects, community energy deliberative budgeting).
This interdisciplinary study is novel in at least two ways. Firstly, we will utilize BDA and the rich, heterogeneous datasets drawing on data from smart sensors, online questionnaires and energy journals, household in-depth interviews, and community engagement activities, to offer householders (electricity consumers) freedom of choice, facilitate them to build community partnerships, empower them to deliberate on energy future options, and ultimately, demonstrating a community-based approach to contributing to the decarbonization of cities. Secondly, our comparative perspective focusing on households with different demographic background will make a significant contribution, enabling us to map the variety of different forms of smart low-carbon communities and establish benchmarks for replication and scaling up of impacts.
This study focuses mainly on qualitative and engagement research, utilising methods such as face-to-face interviews, questionnaire survey, and field observation supplemented with big data analysis.