Identifying American climate change free riders and motivating sustainable behavior.

Free riders, who benefit from collective efforts to mitigate climate change but do not actively contribute, play a key role in shaping behavioral climate action. Using a sample of 2096 registered American voters, we explore the discrepancy between two groups of free riders: cynics, who recognize the significance of environmental issues but do not adopt sustainable behaviors, and doubters, who neither recognize the significance nor engage in such actions. Through statistical analyses, we show these two groups are different. Doubters are predominantly male, younger, with lower income and education, exhibit stronger conspiracy beliefs, lower altruism, and limited environmental knowledge, are more likely to have voted for Trump and lean towards conservative ideology. Cynics are younger, religious, higher in socioeconomic status, environmentally informed, liberal-leaning, and less likely to support Trump. Our research provides insights on who could be most effectively persuaded to make climate-sensitive lifestyle changes and provides recommendations to prompt involvement in individual sustainability behaviors. Our findings suggest that for doubters, incentivizing sustainability through positive incentives, such as financial rewards, may be particularly effective. Conversely, for cynics, we argue that engaging them in more community-driven and social influence initiatives could effectively translate their passive beliefs into active participation.

Attention, sentiments and emotions towards emerging climate technologies on Twitter.

Public perception of emerging climate technologies, such as greenhouse gas removal (GGR) and solar radiation management (SRM), will strongly influence their future development and deployment. Studying perceptions of these technologies with traditional survey methods is challenging, because they are largely unknown to the public. Social media data provides a complementary line of evidence by allowing for retrospective analysis of how individuals share their unsolicited opinions. Our large-scale, comparative study of 1.5 million tweets covers 16 GGR and SRM technologies and uses state-of-the-art deep learning models to show how attention, and expressions of sentiment and emotion developed between 2006 and 2021. We find that in recent years, attention has shifted from general geoengineering themes to specific GGR methods. On the other hand, there is little attention to specific SRM technologies and they often coincide with conspiracy narratives. Sentiments and emotions in GGR tweets tend to be more positive, particularly for methods perceived to be natural, but are more negative when framed in the geoengineering context.

Future workspace needs flexibility and diversity: A machine learning-driven behavioural analysis of co-working space.

The future of workspace is significantly shaped by the advancements in technologies, changes in work patterns and workers' desire for an improved well-being. Co-working space is an alternative workspace solution, for cost-effectiveness, the opportunity for diverse and flexible design and multi-use. This study examined the human-centric design choices using spatial and temporal variation of occupancy levels and user behaviour in a flexible co-working space in London. Through a machine-learning-driven analysis, we investigated the time-dependent patterns, decompose space usage, calculate seat utilisation and identify spatial hotspots. The analysis incorporated a large dataset of sensor-detected occupancy data spanning 477 days, comprising more than 140 million (145×106) data points. Additionally, on-site observations of activities were recorded for 13 days spanning over a year, with 110 time instances including more than 1000 snapshots of occupants' activities, indoor environment, working behaviour and preferences. Results showed that the shared working areas positioned near windows or in more open, connected and visible locations are significantly preferred and utilised for communication and working, and semi-enclosed space on the side with less visibility and higher privacy are preferred for focused working. The flexibility of multi-use opportunity was the most preferred feature for hybrid working. The findings offer data-driven insights for human-centric space planning and design of office spaces in the future, particularly in the context of hybrid working setups, hot-desking and co-working systems.

Conspiracy spillovers and geoengineering.

Geoengineering techniques such as solar radiation management (SRM) could be part of a future technology portfolio to limit global temperature change. However, there is public opposition to research and deployment of SRM technologies. We use 814,924 English-language tweets containing #geoengineering globally over 13 years (2009-2021) to explore public emotions, perceptions, and attitudes toward SRM using natural language processing, deep learning, and network analysis. We find that specific conspiracy theories influence public reactions toward geoengineering, especially regarding "chemtrails" (whereby airplanes allegedly spray poison or modify weather through contrails). Furthermore, conspiracies tend to spillover, shaping regional debates in the UK, USA, India, and Sweden and connecting with broader political considerations. We also find that positive emotions rise on both the global and country scales following events related to SRM governance, and negative and neutral emotions increase following SRM projects and announcements of experiments. Finally, we also find that online toxicity shapes the breadth of spillover effects, further influencing anti-SRM views.

Financing renewable energy: policy insights from Brazil and Nigeria.

Background: Achieving climate targets will require a rapid transition to clean energy. However, renewable energy (RE) firms face financial, policy, and economic barriers to mobilizing sufficient investment in low-carbon technologies, especially in low- and middle-income countries. Here, we analyze the challenges and successes of financing the energy transition in Nigeria and Brazil using three empirically grounded levers: financing environments, channels, and instruments. Results: While Brazil has leveraged innovative policy instruments to mobilize large-scale investment in RE, policy uncertainty and weak financing mechanisms have hindered RE investments in Nigeria. Specifically, Brazil's energy transition has been driven by catalytic finance from the Brazilian Development Bank (BNDES). In contrast, bilateral agencies and multilateral development banks (MDBs) have been the largest financiers of renewables in Nigeria. Policy instruments and public-private partnerships need to be redesigned to attract finance and scale market opportunities for RE project developers in Nigeria. Conclusions: We conclude that robust policy frameworks, a dynamic public bank, strategic deployment of blended finance, and diversification of financing instruments would be essential to accelerate RE investment in Nigeria. Considering the crucial role of donors and MDBs in Nigeria, we propose a multi-stakeholder model to consolidate climate finance and facilitate the country's energy transition.

Do fossil fuel firms reframe online climate and sustainability communication? A data-driven analysis.

Identifying drivers of climate misinformation on social media is crucial to climate action. Misinformation comes in various forms; however, subtler strategies, such as emphasizing favorable interpretations of events or data or reframing conversations to fit preferred narratives, have received little attention. This data-driven paper examines online climate and sustainability communication behavior over 7 years (2014-2021) across three influential stakeholder groups consisting of eight fossil fuel firms (industry), 14 non-governmental organizations (NGOs), and eight inter-governmental organizations (IGOs). We examine historical Twitter interaction data (n = 668,826) using machine learning-driven joint-sentiment topic modeling and vector autoregression to measure online interactions and influences amongst these groups. We report three key findings. First, we find that the stakeholders in our sample are responsive to one another online, especially over topics in their respective areas of domain expertise. Second, the industry is more likely to respond to IGOs' and NGOs' online messaging changes, especially regarding environmental justice and climate action topics. The fossil fuel industry is more likely to discuss public relations, advertising, and corporate sustainability topics. Third, we find that climate change-driven extreme weather events and stock market performance do not significantly affect the patterns of communication among these firms and organizations. In conclusion, we provide a data-driven foundation for understanding the influence of powerful stakeholder groups on shaping the online climate and sustainability information ecosystem around climate change.

Harnessing human and machine intelligence for planetary-level climate action.

The ongoing global race for bigger and better artificial intelligence (AI) systems is expected to have a profound societal and environmental impact by altering job markets, disrupting business models, and enabling new governance and societal welfare structures that can affect global consensus for climate action pathways. However, the current AI systems are trained on biased datasets that could destabilize political agencies impacting climate change mitigation and adaptation decisions and compromise social stability, potentially leading to societal tipping events. Thus, the appropriate design of a less biased AI system that reflects both direct and indirect effects on societies and planetary challenges is a question of paramount importance. In this paper, we tackle the question of data-centric knowledge generation for climate action in ways that minimize biased AI. We argue for the need to co-align a less biased AI with an epistemic web on planetary health challenges for more trustworthy decision-making. A human-in-the-loop AI can be designed to align with three goals. First, it can contribute to a planetary epistemic web that supports climate action. Second, it can directly enable mitigation and adaptation interventions through knowledge of social tipping elements. Finally, it can reduce the data injustices associated with AI pretraining datasets.

Social media enables people-centric climate action in the hard-to-decarbonise building sector.

The building and construction sector accounts for around 39% of global carbon dioxide emissions and remains a hard-to-abate sector. We use a data-driven analysis of global high-level climate action on emissions reduction in the building sector using 256,717 English-language tweets across a 13-year time frame (2009-2021). Using natural language processing and network analysis, we show that public sentiments and emotions on social media are reactive to these climate policy actions. Between 2009-2012, discussions around green building-led emission reduction efforts were highly influential in shaping the online public perceptions of climate action. From 2013 to 2016, communication around low-carbon construction and energy efficiency significantly influenced the online narrative. More significant interactions on net-zero transition, climate tech, circular economy, mass timber housing and climate justice in 2017-2021 shaped the online climate action discourse. We find positive sentiments are more prominent and recurrent and comprise a larger share of the social media conversation. However, we also see a rise in negative sentiment by 30-40% following popular policy events like the IPCC report launches, the Paris Agreement and the EU Green Deal. With greater online engagement and information diffusion, social and environmental justice topics emerge in the online discourse. Continuing such shifts in online climate discourse is pivotal to a more just and people-centric transition in such hard-to-decarbonise sectors.

Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models.

This study evaluates the effect of complete nationwide lockdown in 2020 on residential electricity demand across 13 Indian cities and the role of digitalisation using a public smart meter dataset. We undertake a data-driven approach to explore the energy impacts of work-from-home norms across five dwelling typologies. Our methodology includes climate correction, dimensionality reduction and machine learning-based clustering using Gaussian Mixture Models of daily load curves. Results show that during the lockdown, maximum daily peak demand increased by 150-200% as compared to 2018 and 2019 levels for one room-units (RM1), one bedroom-units (BR1) and two bedroom-units (BR2) which are typical for low- and middle-income families. While the upper-middle- and higher-income dwelling units (i.e., three (3BR) and more-than-three bedroom-units (M3BR)) saw night-time demand rise by almost 44% in 2020, as compared to 2018 and 2019 levels. Our results also showed that new peak demand emerged for the lockdown period for RM1, BR1 and BR2 dwelling typologies. We found that the lack of supporting socioeconomic and climatic data can restrict a comprehensive analysis of demand shocks using similar public datasets, which informed policy implications for India's digitalisation. We further emphasised improving the data quality and reliability for effective data-centric policymaking.

Disruptive innovation for inclusive renewable policy in sub-Saharan Africa: A social shaping of technology analysis of appliance uptake in Rwanda.

Rural off-grid renewable energy solutions often fail due to uncertainties in household energy demand, insufficient community engagement, inappropriate financial models and policy inconsistency. Social shaping of technology (SST) of household appliances provides a critical lens of understanding the involved socio-technical drivers behind these constraints. This study employs an SST lens to investigate appliance uptake drivers in 14,580 households in Rwanda, such that these drivers can aid in policy design for green growth at the grassroots level. The methodology includes an epistemological review of non-income drivers of appliance uptake. Empirical analysis using a binary logistic regression, based on which disruptive innovation pathways were derived for fostering green growth. Results showed that appliance uptake was highly gendered and skewed across the Ubudehe (social welfare) categories. ICT-devices like mobile phones and radios had a higher likelihood of ownership than welfare appliances like refrigerator and laundry machines. Fans and cookers also demonstrated a greater probability of ownership. Disruptive innovation pathways were derived from leveraging the ICT-driven wave of appliance ownership, creation of service sectors through off-grid renewable solutions and promoting cleaner fuel-switching of cooking energy at the household level. Further policy implications were drawn to support the creation of consumption identities for green growth.

Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research.

Text-based data sources like narratives and stories have become increasingly popular as critical insight generator in energy research and social science. However, their implications in policy application usually remain superficial and fail to fully exploit state-of-the-art resources which digital era holds for text analysis. This paper illustrates the potential of deep-narrative analysis in energy policy research using text analysis tools from the cutting-edge domain of computational social sciences, notably topic modelling. We argue that a nested application of topic modelling and grounded theory in narrative analysis promises advances in areas where manual-coding driven narrative analysis has traditionally struggled with directionality biases, scaling, systematisation and repeatability. The nested application of the topic model and the grounded theory goes beyond the frequentist approach of narrative analysis and introduces insight generation capabilities based on the probability distribution of words and topics in a text corpus. In this manner, our proposed methodology deconstructs the corpus and enables the analyst to answer research questions based on the foundational element of the text data structure. We verify theoretical compatibility through a meta-analysis of a state-of-the-art bibliographic database on energy policy, narratives and computational social science. Furthermore, we establish a proof-of-concept using a narrative-based case study on energy externalities in slum rehabilitation housing in Mumbai, India. We find that the nested application contributes to the literature gap on the need for multidisciplinary methodologies that can systematically include qualitative evidence into policymaking.

REST framework: A modelling approach towards cooling energy stress mitigation plans for future cities in warming Global South.

Future cities of the Global South will not only rapidly urbanise but will also get warmer from climate change and urbanisation induced effects. It will trigger a multi-fold increase in cooling demand, especially at a residential level, mitigation to which remains a policy and research gap. This study forwards a novel residential energy stress mitigation framework called REST to estimate warming climate-induced energy stress in residential buildings using a GIS-driven urban heat island and energy modelling approach. REST further estimates rooftop solar potential to enable solar photo-voltaic (PV) based decentralised energy solutions and establish an optimised routine for peer-to-peer energy sharing at a neighbourhood scale. The optimised network is classified through a decision tree algorithm to derive sustainability rules for mitigating energy stress at an urban planning scale. These sustainability rules established distributive energy justice variables in urban planning context. The REST framework is applied as a proof-of-concept on a future smart city of India, named Amaravati. Results show that cooling energy stress can be reduced by 80 % in the study area through sensitive use of planning variables like Floor Space Index (FSI) and built-up density. It has crucial policy implications towards the design and implementation of a national level cooling action plans in the future cities of the Global South to meet the UN-SDG - 7 (clean and affordable energy) and SDG - 11 (sustainable cities and communities) targets.

India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling.

India locked down 1.3 billion people on March 25, 2020, in the wake of COVID-19 pandemic. The economic cost of it was estimated at USD 98 billion, while the social costs are still unknown. This study investigated how government formed reactive policies to fight coronavirus across its policy sectors. Primary data was collected from the Press Information Bureau (PIB) in the form press releases of government plans, policies, programme initiatives and achievements. A text corpus of 260,852 words was created from 396 documents from the PIB. An unsupervised machine-based topic modelling using Latent Dirichlet Allocation (LDA) algorithm was performed on the text corpus. It was done to extract high probability topics in the policy sectors. The interpretation of the extracted topics was made through a nudge theoretic lens to derive the critical policy heuristics of the government. Results showed that most interventions were targeted to generate endogenous nudge by using external triggers. Notably, the nudges from the Prime Minister of India was critical in creating herd effect on lockdown and social distancing norms across the nation. A similar effect was also observed around the public health (e.g., masks in public spaces; Yoga and Ayurveda for immunity), transport (e.g., old trains converted to isolation wards), micro, small and medium enterprises (e.g., rapid production of PPE and masks), science and technology sector (e.g., diagnostic kits, robots and nano-technology), home affairs (e.g., surveillance and lockdown), urban (e.g. drones, GIS-tools) and education (e.g., online learning). A conclusion was drawn on leveraging these heuristics are crucial for lockdown easement planning.

How does slum rehabilitation influence appliance ownership? A structural model of non-income drivers.

This study explores the effect of slum rehabilitation on appliance ownership and its implications on residential electricity demand. The low-income scenario makes it unique because the entire proposition is based on the importance of non-income drivers of appliance ownership that includes effects of changing the built environment (BE), household practices (HP) and appliances characteristics (AC). This study demonstrates quantitatively that non-income factors around energy practices influence appliance ownership, and therefore electricity consumption. The methodology consists of questionnaire design across the dimension of BE, HP and AC based on social practice theory, surveying of 1224 households and empirical analysis using covariance-based structural equation modelling. Results show that higher appliance ownership in the slum rehabilitation housing is due to change in household practice, built environment and affordability criteria of the appliances. Change in HP shifts necessary activities like cooking, washing and cleaning from outdoor to indoor spaces that positively and significantly influences higher appliance ownership. Poor BE conditions about indoor air quality, thermal comfort and hygiene; and product cost, discounts and ease of use of the appliances also triggers higher appliance ownership. The findings of this study can aid in designing better regulatory and energy efficiency policies for low-income settlements.

Discomfort and distress in slum rehabilitation: Investigating a rebound phenomenon using a backcasting approach.

Slum rehabilitation policies in India is observed to have a rebound effect on the occupants, where rehabilitated occupants move back to the horizontal slums. In this study, we investigate the cause behind this rebound phenomenon based on a theory of homeostasis, where the loss of homeostasis refers to occupants' heightened discomfort and distress in their built environment. A novel methodological framework was developed to investigate it based on the principles of participatory backcasting approach and the theory of homeostasis. Thirty households in Mumbai's slum rehabilitation housing were interviewed to determine the social, economic and environmental cause of distress and discomfort. Granular information was obtained by further investigating the factors that influence occupants' attitude, emotions, health, control and habits in their built environment that regulates their holistic comfort and lack of stress. The causal linkages among these factors were established using a qualitative fault tree. Results show two primary cause of distress and discomfort in the study area owing to economic distress and built environment related discomfort. Economic distress was from low-income and high electricity bills due to higher household appliance ownership, and built environment discomfort was due to lack of social spaces and poor design of the slum rehabilitation housing. This study showed that mitigating such non-income drivers of distress and discomfort can prevent rebound phenomenon and improve the sustainability of the slum rehabilitation process.

Evolution of sustainable energy policies in India since 1947: A review.

India's Intended Nationally Determined Contributions in 2015 toward the Two-Degree Celsius climate change goal has endorsed 15% of renewable integration in the primary energy mix by 2020. The energy space is strategy to meet the target without affecting its immediate sustainable development goals. This study documents this strategic effort by tracking the historical trajectory of energy policy planning since its independence in 1947. An objective ontological approach was adopted in reviewing the evolution of energy policy into five distinct phases. Phase I (1947-1970), focused on supply adequacy with the overall thrust on infrastructure development as the pillar of Indian economy. In Phase II (the 1970s) the focus shifted in addressing the energy access crisis. Phase III (the 1980s) was based on increment, diversification, and streamlining on supplies for energy security purposes. Phase IV (the 1990s) is the period of modernization of the overall Indian electricity system. Phase V (the 2000s) is the present phase of market transformation and climate change mitigation energy policies. A co-assessment of India's policy to the international climate negotiations showed that India remained responsive to international climate goals. It became reactive in the planning for sustainable energy policy after its ratification of Kyoto Protocol in 2001. Since then, India has been instrumental in administering strict emission reduction norms and efficiency measures. This review concludes that the country needs to upgrade its inefficient transmission and distribution networks, which was broadly neglected. The subsidy allocations in domestic energy resources should be well-adjusted without compromising on its social costs.