Skip to content

A sentiment analysis of public opposition to carbon capture and storage technology

Sep 2026 · International journal of greenhouse gas control · 57 references
Social Acceptance of Renewable Energy

Abstract

Carbon capture and storage (CCS) is recognized as an important technology for reducing greenhouse gas emissions and achieving climate targets, yet some segments of society express public opposition. Public opposition, even when expressed by minority groups, has at times influenced CCS project outcomes. Opposition extends beyond technical risk concerns, including emotional, symbolic, and territorial dimensions. This study examines how such opposition was constructed through protest materials, social media content, and press articles related to a specific CCS project. A mixed-methods design combined sentiment analysis using a natural language processing (NLP) model with qualitative content analysis. Because the corpus was deliberately sampled from oppositional channels, these sentiment scores characterize how opposition is expressed rather than the balance of public opinion at large. Within this corpus, opposition was articulated largely through negative sentiment (mean sentiment -0.44), particularly in materials from environmental NGOs and labor unions, with recurring concerns including seismic risk, proximity, institutional distrust, and perceived territorial injustice. On the qualitative side, protesters frequently employed emotionally charged metaphors (e.g., “ticking bomb”, “waste”) and comparisons to other CCS projects, reframing the technology as an imposition rather than a climate solution. Nonetheless, the dataset also captured neutral and occasionally supportive tones, especially in citizen and press discourse, indicating that while opposition dominated, public responses were not monolithic. These findings contribute to a more nuanced understanding of CCS acceptance by highlighting the emotional, symbolic, and communicative dynamics of opposition. The study also demonstrates the value of sentiment analysis for capturing discursive opposition in contested projects. Moreover, the results underline that the social acceptance of CCS is shaped as much by identity, symbolism, and public discourse as by technical and regulatory considerations.

Read PDF

Similar papers

#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Conference Open access Dec 2013

Affordable and Energy-Efficient Cloud Computing Clusters: The Bolzano Raspberry Pi Cloud Cluster Experiment

The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.

P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al. · 110 citations · ⚡7
#computer vision Book Open access Mar 2017

On the Unhappiness of Software Developers

The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.

D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al. · 84 citations · ⚡6

Related blog posts

MIT News · Artificial Intelligence Sep 14, 2026

New method enables AI for safety-critical situations

The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.

GPT-Lab Sep 10, 2026

Responsible AI Must Consider Its Afterlife

AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.