Growing climate change risk concerns with rising regional disparities in China.

This study presents a high-resolution mapping of climate change perceptions across China, highlighting the evolution of public perception regarding the priority and impact of climate change over a 13-year period between 2010 and 2023. Utilizing data from two national surveys conducted ( = 11783 and  = 4050), we show a considerable rise in the perceived priority (19%) and impact (13%) of climate change issues nationally, alongside growing regional disparities. We do robustness checks of our results using repeated simulations between multilevel regression and poststratification and disaggregation methods.

Partisanship overcomes framing in shaping solar geoengineering perceptions: Evidence from a conjoint experiment.

The discourse on solar geoengineering (SG) is evolving, yet public perceptions of SG as a climate change solution remain underexplored, especially in the polarized US political landscape. We examine the relative importance of different SG narratives-framed as complementary, substitutive, or posing a moral hazard-and partisan information sources in shaping public attitudes. Using a conjoint experiment with 2123 American voters, we find that partisan alignment with the information source plays a decisive role in shaping trust in the messenger and support for SG, overshadowing any impact of message framing.

Perceptions of science, science communication, and climate change attitudes in 68 countries - the TISP dataset.

Science is integral to society because it can inform individual, government, corporate, and civil society decision-making on issues such as public health, new technologies or climate change. Yet, public distrust and populist sentiment challenge the relationship between science and society. To help researchers analyse the science-society nexus across different geographical and cultural contexts, we undertook a cross-sectional population survey resulting in a dataset of 71,922 participants in 68 countries.

A global fairtrade partnership needed to address injustices in the supply chains of clean energy technology materials.

Abstract: Renewable sources produced close to one-third of the world's electricity in 2023. However, a limited but growing body of research suggests rapid renewable energy development is leading to conflict and resource exploitation in energy-transitioning communities. Such injustices are attributable to the extractivist nature of renewable energy development, where raw materials, also known as Clean Energy Technology Materials (CETMs), are in limited quantities and often concentrated in resource-constrained zones in the Global South.

Underestimation of personal carbon footprint inequality in four diverse countries.

Extensive research highlights global and within-country inequality in personal carbon footprints. However, the extent to which people are aware of these inequalities remains unclear. Here we use an online survey distributed across four diverse countries: Denmark, India, Nigeria and the USA, to show widespread underestimation of carbon footprint inequality, irrespective of participants' country and income segment.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.