Our work on net zero energy systems: what do our publications say about us?

Introducing NexSys research through the lens of publication abstracts

by Brian Boyle and Stefan Müller

Brian Boyle is a Postdoctoral Researcher in the School of Politics and International Relations at University College Dublin. Brian’s main research interests include social inequalities and representation in political behaviour and political communication, with a focus on the use of quantitative and computational social science approaches.

Stefan Müller is an Assistant Professor and Ad Astra Fellow in the School of Politics and International Relations at University College Dublin. His research focuses on political representation, party competition, political communication, public opinion, and quantitative text analysis. Stefan is a core member of the Connected_Politics Lab, a fellow at the UCD Geary Institute for Public Policy, a member of the UCD Energy Institute, co-author of the quanteda R package, and maintainer of the Irish Polling Indicator. He established the Text & Policy Research Group at UCD.

As political and computational social scientists, at NexSys we are exploring how researchers, parties, politicians, interest groups, and legal documents discuss net-zero emissions policies. Using quantitative text analysis and supervised machine learning, we will define and map policies relating to the environment and sustainability, and provide recommendations for policymakers. This brief analysis forms part of our broader work within NexSys.

The authors

Keen to learn more about NexSys and unsure where to start? In this blog post, we use bibliometrics, the statistical analysis of publications, to introduce the NexSys team and their research. 

We were curious to find out how our team’s prior work relates to the core aims of the NexSys programme, and which issues relating to the strands of NexSys have been addressed in past abstracts of publications. Our results illustrate the depth and breadth of NexSys research.

Over 2,600 publications from ten different subject areas

We systematically collected information on previous academic publications from the NexSys team. The Elsevier Scopus database contains abstracts and citation information on over 85 million documents, across more than 25,000 peer-reviewed journals, books, and conference papers. Of the 74 NexSys team members listed on the project staff page [data collected in January 2023], 59 were present in the Scopus database. This covered all staff members with the exception of our PhD Students and non-academic members of the team.

While over half of NexSys researchers have an engineering background, the full team covers ten different subject areas, including architecture, computer science, economics, and social policy. The engineers themselves come from over seven sub-fields, including chemical, civil, electrical, marine, and mechanical engineering.

Searching the Scopus database by author returned 3,200 publications, 2,880 of which contained a valid digital object identifier (DOI), and relevant summary description text (e.g. article abstracts). The NexSys members’ publications were spread across a variety of formats, including 2,000 journal articles, 600 conference papers, as well as 160 books and book chapters.

Differences Across Disciplines

We provide descriptive analyses of publication abstracts using the quanteda R package (Benoit et al. 2018) for quantitative text analysis. The table below lists the number of abstracts from each subject area. We also report the abstracts’ most frequent terms and phrases, after removing punctuation characters, numbers, and so-called “stopwords” which appear in almost all scientific publications. 

The list underscores the depth of our research, but also shows that researchers from most disciplines have directly worked on one or more of the core issues of the NexSys programme.

Most Frequent Features in Publication Abstract by Subject Area

  • Architecture (136 abstracts): nbs (62 mentions), performance (54), impact (48), ireland (43), building (43), design (36), monitoring (36), urban (35), ess (35), concrete (33)
  • Business (127 abstracts): ireland (37 mentions), policy (35), consumers (34), energy (33), social (33), research (28), firms (28), policies (27), acceptability (26), demand (25)
  • Chemical Engineering (176 abstracts): biofilm (138 mentions), biofilms (69), membrane (69), process (63), potential (55), system (54), high (53), oxygen (49), gas (48), treatment (46)
  • Civil/Structural Engineering (409 abstracts): ireland (156 mentions), bridge (139), potential (134), cycling (125), vehicle (120), water (114), transport (110), new (107), performance (100), system (100)
  • Computer Science (254 abstracts): system (173 mentions), performance (107), potential (78), systems (75), process (71), technology (71), students (69), learning (65), technologies (65), biomass (65)
  • Economics (51 abstracts): carriers (38 mentions), policy (22), network (20), period (20), networks (18), airports (18), european (17), factors (16), regional (16), costs (16)
  • Electrical Engineering (759 abstracts): system (325 mentions), control (220), power_system (166), power_systems (165), ieee (152), impact (147), frequency (145), power (141), network (131), proposes (129)
  • Geography (61 abstracts): urban (105 mentions), cities (64), lcz (39), climate (37), city (28), surface (26), studies (24), wudapt (23), canyon (23), urban_areas (22)
  • Marine Engineering (54 abstracts): wave (49 mentions), responses (45), response (37), wind (33), design (32), platform (32), compared (28), motion (27), wind_turbine (26), present (25)
  • Mathematics (35 abstracts): ireland (22 mentions), flow (17), temperature (16), increase (15), future (15), applied (15), winter (14), observed (13), period (13), forecast (13)
  • Mechanical Engineering (598 abstracts): building (166 mentions), performance (156), experimental (148), system (127), compared (125), potential (118), damage (112), flow (112), numerical (112), energy (107)
  • Other/Non-academic (187 abstracts): system (68 mentions), adaptation (55), ieee (54), network (53), voltage (52), impact (49), methodology (48), control (46), load (45), power_systems (44)
  • Politics and Social Policy (33 abstracts): housing (48 mentions), parties (24), voters (23), policy (20), social (17), electoral (15), support (14), problems (13), government (13), party (12)

The Focus on the Five Strands

NexSys consists of five strands: four hub strands (Water; Cities and Communities; Transport; Offshore Wind), and the Energy Systems core strand linking these four areas.

We explore how the NexSys team’s research fits into each strand (due to the overarching scope of the Energy Systems core strand, this was excluded from the current analysis). In order to classify the database of publication abstracts, we used a two-stage procedure. First, we selected ‘seed words’ that were narrowly and directly related to each strand (water; cities, city, community; transport, infrastructure; wind, offshore wind). We then checked whether or not an abstract contained none, one, or more than one of these keywords. 

With this initial simple classification, we moved on to so-called keyness analysis, a method through which frequent words can be identified (see, e.g., Bondi and Scott 2010; Zollinger 2022). Taking each strand in turn, we set the abstract texts that were identified as belonging to that strand based on the dictionary search as our target category, and all other abstract texts as the reference group. We then compared the relative frequency of features (this could be words or multi-word expressions) across each set of documents and identified words strongly associated with a specific category.

Words that occur very often in documents in our target category, but do not appear much in any of the reference documents, would produce a relatively high Chi2 value. Words that appear frequently in the reference documents, but not very often in the target documents would contain negative values.

From the keyness analysis, we took the ten most distinctive features for each strand and ran new dictionary searches using this expanded set of keywords to re-classify the publication abstracts, which are outlined below.

Strand Classification – Keyness Analysis 10 Most Distinct Features

  • Water (525 abstracts): water; wastewater; wwtps; wastewater treatment; wastewater treatment plants; cod; treatment; reactor; leachate; underwater
  • Transport (981 abstracts): transport; infrastructure; cycling; transportation; public transport; travel; car; cyclists; vehicles; avs
  • Cities (1019 abstracts): capacity; electricity; cities; city; velocity; urban; community; communities; lcz; market
  • Wind (529 abstracts): wind; wind turbine; wind power; wind generation; wind speed; wind turbines; wind farms; wind farm; wind power generation; turbine

After this second round of classification, we took each set of abstracts labelled under each strand and then ran a final keyness analysis. For abstracts falling into each of the four strands, we compared publications by engineers with researchers across all other subject areas. This allows us to explore how our team’s overall previous research aligns with the NexSys strands. The keyness analysis also helps us understand how the focus on each strand differs across research fields.

The results for each strand are displayed in the figures below. 

Broadly speaking, we observe clear differences in terms of both the language used across each of the four strands, as well as between engineering and other disciplines’ focus within each strand.

Engineering research tends to have a more focused scope, that is directly tied to concepts surrounding measurement, technology, and physical systems. The remaining research fields, meanwhile, tend to relate to a somewhat broader level of analysis, with the most distinct terms focusing on people-centred aspects of each strand. Examples include urban areas, specific locations and places, and the human impact of climate change. 

This very preliminary result highlights that NexSys researchers have focused on a combination of the technical questions and societal effects of these technologies. 

What we learned

Exploring the abstract texts from the NexSys team’s previous publications highlights the breadth of research experience and knowledge offered within the programme. It is clear that bringing together researchers from a broad range of subject areas is a key advantage of NexSys in its endeavour to develop technical, political, and social solutions to reach our net zero energy goals. This initial textual analysis shows how NexSys addresses the programme’s core objectives from various angles.

References

Benoit, Kenneth, Kohei Watanabe, Haiyan Wang, Paul Nulty, Adam Obeng, Stefan Müller, and Akitaka Matsuo. 2018. “Quanteda: An R Package for the Quantitative Analysis of Textual Data.” The Journal of Open Source Software 3 (30): 774. https://doi.org/10.21105/joss.00774

Bondi, Marina, and Mike Scott, eds. 2010. Keyness in Texts. Amsterdam: John Benjamins. https://doi.org/10.1075/scl.41

Zollinger, Delia. 2022. “Cleavage Identities in Voters’ Own Words: Harnessing Open-Ended Survey Responses.” American Journal of Political Science published ahead of print. https://doi.org/10.1111/ajps.12743

About NexSys

Next Generation Energy Systems (NexSys) is an all-island multidisciplinary research programme, involving nine different research institutions, alongside industry partners from across the energy sector. The programme’s key aims include tackling the challenges of energy decarbonisation, and developing evidence-based pathways for a just, net-zero energy system.

Why are bottom-up approaches to renewable energy more acceptable for communities than top-down approaches?

Assoc. Prof. Geertje Schuitema gives an overview of the findings and significance of her research group’s recent publication on Just Transition in the publication Energy Research & Social Science.

“Bottom-up approaches strengthen collective psychological ownership and perceptions of place-technology fit among communities where renewable energy projects are being developed”

Assoc. Prof. Geertje Schuitema, NexSys academic

 

Vanja Međugorac and Geertje Schuitema.

Why is bottom-up more acceptable than top-down? A study on collective psychological ownership and place-technology fit in the Irish Midlands. 

DOI: https://doi.org/10.1016/j.erss.2022.102924 

 

It is well known that communities tend to policies proposal by government or industry (so called top-down governance approaches) usually less acceptable than policy proposals that are developed by communities themselves (so-called bottom-up governance approaches).

The aim of this paper was to understand why this is the case. We did a survey study in Lanesborough, a town in the Irish Midlands, which is in transition from a region that heavily depends on fossil fuel production (peat) and is earmarked to become a region for renewable energy.

Community responses

We compared community responses of two existing plans for future development of the region: the development of wind energy which was proposed by Bord na Mona (top-down) and the rewetting of the peatlands including a solar park (bottom-up).

Our findings

We found that the bottom-up approach was more acceptable for communities for two reasons.

    • Firstly, bottom-up approach resulted in a feeling of collective psychological ownership, that is, communities feel that these plans and developments are “theirs”.
    • This feeling of psychological ownership, in turn, meant that communities felt that the development plans fitted much better in the community, which is why they found them more acceptable.

These results suggest that it is important to structure governance processes in such a way that it fosters collective psychological ownership over renewable energy developments. 

How can this be achieved ? This can for example be achieved by using local knowledge and ensuring that public engagement and participation is part of the governance process.

Academic Profile: Assoc. Prof. Geertje Schuitema https://people.ucd.ie/geertje.schuitema 

 

Details of Publication:

Vanja Međugorac, Geertje Schuitema,

Why is bottom-up more acceptable than top-down? A study on collective psychological ownership and place-technology fit in the Irish Midlands.

Energy Research & Social Science, Volume 96, 2023, 102924, ISSN 2214-6296, https://doi.org/10.1016/j.erss.2022.102924. (https://www.sciencedirect.com/science/article/pii/S2214629622004273) 

 

Abstract: Previous research has shown that bottom-up governance approaches enjoy higher community acceptance than top-down approaches. However, it is unclear why this is the case. We investigated this in a survey-based field study in a community in the Irish Midlands that is transitioning away from fossil fuel-based (peat) based energy generation to a renewable energy system. Community members evaluated two scenarios that were part of the actual public debate, that is – a scenario proposed by the government and industry (a top-down scenario), and a scenario proposed by some local community members (a bottom-up scenario). The results showed that, compared to the top-down scenario, the bottom-up scenario was more acceptable, community members felt stronger collective psychological ownership over it, and it was perceived as more place-fitting. Mediation analysis confirmed that higher community acceptance of the bottom-up scenario compared to the top-down one was mediated by stronger feelings of collective psychological ownership and perceptions of place-technology fit community members had regarding the proposed bottom-up development than the top-down one. These results imply that community acceptance is higher under bottom-up governance approaches as they strengthen collective psychological ownership and perceptions of place-technology fit among communities where renewable energy projects are being developed.

 

Keywords: Community acceptance; Top-down governance; Bottom-up governance; Collective psychological ownership; Place-technology fit; Renewable energy developments