Ricardo Vinuesa
Biographic Data
| ID | 2744931 |
|---|---|
| NAME | Ricardo Vinuesa |
| GIVEN NAMES | Ricardo |
| FAMILY NAME | Vinuesa |
| SIGNATURE | VINUESA R |
| AFFILIATIONS | KTH Royal Institute of Technology |
| ORCID | 0000-0001-6570-5499 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 28 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 3 |
Large language models in climate and sustainability policy: Limits and opportunities
Accurate, reliable and updated information support effective decision-making by reducing uncertainty and enabling informed choices. Multiple crises threaten the sustainability of our societies and pose at risk the planetary boundaries, hence requiring usable and operational knowledge. Natural-language processing tools facilitate data collection, extraction and analysis processes. They expand knowledge utilization capabilities by improving access …
Integrating sustainable development goals into life cycle thinking: A multidimensional approach for advancing sustainability
Achieving the UN’s Sustainable Development Goals (SDGs) by 2030 remains a significant challenge, with 30% of targets showing no progress, regression, or severe deviations. Life Cycle Thinking (LCT) is a valuable method for monitoring and accelerating progress toward sustainable development by evaluating systems, processes, or products from environmental, economic, and societal perspectives. This work provides insights for integrating SDGs into LC…
Artificial intelligence for Sustainable Development Goals: Bibliometric patterns and concept evolution trajectories
The development of artificial intelligence (AI) as a field has impacted almost all aspects of human life. More recently it has found a role in addressing developmental challenges, specifically the Sustainable Development Goals (SDGs). However, there are not enough systematic studies on analysis of the role of AI research towards the SDGs. Therefore, this article attempts to bridge this gap by identifying the major bibliometric trends and concept‐…
Artificial intelligence and sustainable development goals nexus via four vantage points
Artificial Intelligence (AI) should aim at benefiting society, the economy, and the environment, i.e., AI should aim to be socially good. The UN-defined Sustainable Development Goals (SDGs) are the best depiction to measure social good. For AI to be socially good, it must support all 17 UN SDGs. Our work provides a unique insight into AI on all fronts including Curricula, Frameworks, Projects, and Research papers. We then analyze these datasets t…
Data deprivations, data gaps and digital divides: Lessons from the Covid-19 pandemic
This paper draws lessons from the COVID-19 pandemic for the relationship between data-driven decision making and global development. The lessons are that (i) users should keep in mind the shifting value of data during a crisis, and the pitfalls its use can create; (ii) predictions carry costs in terms of inertia, overreaction and herding behaviour; (iii) data can be devalued by digital and data deluges; (iv) lack of interoperability and difficult…
The role of artificial intelligence in achieving the Sustainable Development Goals
The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI ne…
Artificial intelligence for Sustainable Development Goals: Bibliometric patterns and concept evolution trajectories
The development of artificial intelligence (AI) as a field has impacted almost all aspects of human life. More recently it has found a role in addressing developmental challenges, specifically the Sustainable Development Goals (SDGs). However, there are not enough systematic studies on analysis of the role of AI research towards the SDGs. Therefore, this article attempts to bridge this gap by identifying the major bibliometric trends and concept‐…
Artificial intelligence and sustainable development goals nexus via four vantage points
Artificial Intelligence (AI) should aim at benefiting society, the economy, and the environment, i.e., AI should aim to be socially good. The UN-defined Sustainable Development Goals (SDGs) are the best depiction to measure social good. For AI to be socially good, it must support all 17 UN SDGs. Our work provides a unique insight into AI on all fronts including Curricula, Frameworks, Projects, and Research papers. We then analyze these datasets t…
Data deprivations, data gaps and digital divides: Lessons from the Covid-19 pandemic
This paper draws lessons from the COVID-19 pandemic for the relationship between data-driven decision making and global development. The lessons are that (i) users should keep in mind the shifting value of data during a crisis, and the pitfalls its use can create; (ii) predictions carry costs in terms of inertia, overreaction and herding behaviour; (iii) data can be devalued by digital and data deluges; (iv) lack of interoperability and difficult…
The role of artificial intelligence in achieving the Sustainable Development Goals
The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI ne…
Data deprivations, data gaps and digital divides: Lessons from the Covid-19 pandemic
This paper draws lessons from the COVID-19 pandemic for the relationship between data-driven decision making and global development. The lessons are that (i) users should keep in mind the shifting value of data during a crisis, and the pitfalls its use can create; (ii) predictions carry costs in terms of inertia, overreaction and herding behaviour; (iii) data can be devalued by digital and data deluges; (iv) lack of interoperability and difficult…
Artificial intelligence for Sustainable Development Goals: Bibliometric patterns and concept evolution trajectories
The development of artificial intelligence (AI) as a field has impacted almost all aspects of human life. More recently it has found a role in addressing developmental challenges, specifically the Sustainable Development Goals (SDGs). However, there are not enough systematic studies on analysis of the role of AI research towards the SDGs. Therefore, this article attempts to bridge this gap by identifying the major bibliometric trends and concept‐…
Artificial intelligence and sustainable development goals nexus via four vantage points
Artificial Intelligence (AI) should aim at benefiting society, the economy, and the environment, i.e., AI should aim to be socially good. The UN-defined Sustainable Development Goals (SDGs) are the best depiction to measure social good. For AI to be socially good, it must support all 17 UN SDGs. Our work provides a unique insight into AI on all fronts including Curricula, Frameworks, Projects, and Research papers. We then analyze these datasets t…
Large language models in climate and sustainability policy: Limits and opportunities
Accurate, reliable and updated information support effective decision-making by reducing uncertainty and enabling informed choices. Multiple crises threaten the sustainability of our societies and pose at risk the planetary boundaries, hence requiring usable and operational knowledge. Natural-language processing tools facilitate data collection, extraction and analysis processes. They expand knowledge utilization capabilities by improving access …
Integrating sustainable development goals into life cycle thinking: A multidimensional approach for advancing sustainability
Achieving the UN’s Sustainable Development Goals (SDGs) by 2030 remains a significant challenge, with 30% of targets showing no progress, regression, or severe deviations. Life Cycle Thinking (LCT) is a valuable method for monitoring and accelerating progress toward sustainable development by evaluating systems, processes, or products from environmental, economic, and societal perspectives. This work provides insights for integrating SDGs into LC…
Computer Science (3 works) · Data science (3 works) · Political science (3 works) · Sustainable development (3 works) · Artificial Intelligence (2 works) · Ecology (2 works) · Economics (2 works) · Engineering (2 works) · Environmental resource management (2 works) · Management science (2 works)