The SEG@COP26 Seminar Series: Oct 19 – Nov 16

In November, the UK hosts the delayed United Nations COP26 climate change summit. During the build-up, and over the COP itself, the Sussex Energy Group will be presenting highlights of SPRU research in the SEG@COP26 seminar series that are relevant to the themes of these crucial global negotiations. The series encompasses the breadth of sustainability topics explored by SEG, and across SPRU, which aim to understand and foster transitions towards sustainable and equitable energy systems.

You can see the full list of events below, and we hope to see many of you there.

Decarbonisation and its discontents – Oct 19 13.00-14.00

Sussex Energy Group Director Benjamin K. Sovacool outlines a critical justice perspective on four low-carbon transitions. What are the types of injustices associated with low-carbon transitions?  In what ways do low-carbon transitions worsen social risks or vulnerabilities?  And what policies might be deployed to make these transitions more just?

Transformative outcomes & a learning game about system changes – Oct 26 13.00-14.00

Bipashyee Ghosh and Imogen Wade explain how the Transformative Innovation Policy Consortium mobilises the power of innovation to address United Nations’ Sustainable Development Goals, and how a learning game can express the complexities of change in complex socio-technical systems. Transformative Innovation Policy is a new framing for innovation policy that supports the race to net zero and towards a sustainability transition for the world.

Carbon Intensive Regions in Transition (CINTRAN) – Nov 2 13.00-14.00

Lean about CINTRAN’s study of the complex patterns and dynamics of structural change in carbon-intensive regions across Europe. CINTRAN’s work provides insight into how different actors are responding to, or “coping” with, decarbonisation policy. Marie Claire Brisbois and Roberto Cantoni outline how this provides a basis for encouraging coping strategies that advance decarbonisation efforts while addressing the needs of actors for decent, sustainable livelihoods.

Just Transitions: the governance and institutional gap in the UK – Nov 9 13.00 -14.00

What are the challenges of governance for just transitions? The notion of “just transition” is now a central feature of climate policymaking at various levels transnationally—from the local to the UN climate change negotiations, with nation states being urged to consider this policy issue in national climate policy frameworks since the 2018 Silesia Declaration at COP24. Abigail Martin and Max Lacey-Barnacle discuss how the growing calls for a green recovery from COVID-19 have placed economic inequality and climate justice at the top of the political agenda in many countries, adding pressure to COP26 delegates and policymakers to deliver a ‘just transition’.

Panel Discussion – Reactions to COP26 outcomes – Nov 16 13.00-14.00

A panel of Sussex Energy Group researchers discuss the outcomes of COP26, the debates that have come up throughout this seminar series, and what bearing the outcomes from the climate negotiations may have on their research interests. Panelists are: Marie Claire Brisbois, Ralitsa Hiteva, Lokendra Karki and Phil Johnstone.

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New Co-Director for the Sussex Energy Group

Dr Mari Martiskainen has joined the leadership team of the Sussex Energy Group (SEG) as Co-Director. Professor Karoline Rogge has stepped down as Co-Director to focus on the demands of her expanding research portfolio such as the new EMPOCI project, and will remain an important member of SEG. 

SEG is a long established, growing group of researchers at Sussex who share the aim of understanding and fostering transitions towards sustainable, low carbon energy systems.

The group undertakes academically rigorous, interdisciplinary and world-leading research that is relevant to contemporary policy challenges, as well as educating the next generation of energy policy professionals through MSc and PhD programmes.

The group is directed by Professor Benjamin K. Sovacool and co-directed by Dr Marie-Claire Brisbois and Dr Mari Martiskainen.

Dr Mari Martiskainen is a social scientist whose work focuses on sustainability transitions, with a particular focus on strategies for ensuring that net zero energy, housing and transport systems create a just society for everyone. She joined SPRU as a Research Officer in 2006, and since then has researched a wide range of topics including the energy justice implications of low carbon transitions, development of low energy housing, community action on fuel poverty, and the rollout of renewable energy technologies.

Prof Benjamin Sovacool, Director of SEG:

“Professor Rogge leaves some very large shoes to fill. She has done a wonderful job acting as co-director over a period of rapid growth for SEG. I fully expect Dr Martiskainen to be an equally wonderful co-director, as evidenced already by her leadership in the FAIR project within SEG. She is an all-round exceptionally capable researcher and a pleasure to work with.”

Dr Marie-Claire Brisbois, Co-Director of SEG:

“Working with Professor Rogge on the SEG Directorship team has been a privilege, and I would like to thank her for her dedication and leadership as she transitions to her new role as a SEG member. I’d also like to warmly welcome Dr Martiskainen to the Directorship team. I look forward to working with her to further the quality and depth of energy research at the University of Sussex, as organised through SEG”.

Dr Martiskainen said:

“I am delighted to be starting as SEG Co-Director and working with one of the most innovative energy research groups in the world. Our over 50-year legacy means that I am taking this role on humbly, and it is an honour to follow in the footsteps of scholars like Professor Karoline Rogge, to whom I am immensely grateful for her intellectual inspiration and collegiality. SEG has a diverse team of excellent academics and educators, and we do not shy away from asking challenging research questions. The urgency of climate change – and our energy system’s role in that – means that our field is going to be ever more important in helping to figure out how we can adapt to the changes needed, and do it so that everyone benefits from a net zero society.“


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A dialogue on examining datasets in the nuclear vs renewable energy debate

Image of cooling towers against horizon

Following the publication of their paper Differences in carbon emissions reduction between countries pursuing renewable electricity versus nuclear powerin Nature Energy last October, Prof. Benjamin K Sovacool, Prof. Andy Stirling and their co-authors received a number of responses and challenges to the paper’s findings.

To advance scientific debate around independent research, they engaged in a series of dialogues with researchers offering critiques of our work. Below, they share an exchange with Daniel Perez, PhD student at École Normale Supérieure in Paris.

Mr Perez’s paper, On Sovacool’s et al. study on the differences in carbon emissions reduction between countries pursuing renewable electricity versus nuclear power, offers a critical perspective of Sovacool et al.’s paper’s models and statistical analysis.

The exchange below begins with their response to Mr Perez’s paper, followed by Mr Perez’s response to theirs.

By sharing the exchange here, Profs Sovacool and Stirling hope to encourage collegiate debate and support the critical importance of independent research, an issue considered in their earlier blog, Nuclear vs renewable energy and the critical importance of independent research.

Thanks to Mr Perez for his original response and for participating in this exchange.


Response to Daniel Perez’s Matters Arising

Benjamin K. Sovacool, Patrick Schmid, Andy Stirling, Goetz Walter & Gordon MacKerron

We thank Mr. Perez for engaging with our article. But we do not believe any of the concerns he raises are novel, nor do they hit the point on many aspects.

First of all, if he had read thoroughly, Mr. Perez might have noticed that we never talk about greenhouse gases in their full generality. To say we use GDP to “confound something” is a serious misrepresentation. We actually use GDP as a “control”.

Mr. Perez also seems to misunderstand us when he says: “despite the fact that decarbonated energy sources are not good predictors of GHG emissions” and “Fossil fuels as the real predictor and the ‘crowding out’ hypothesis”. Just as we never address GHG in their entirety, so we never claim that clean energy sources are a “predictor” of CO2 emissions. Ours is not a predictive but a correlative study. The reader might wonder why Mr. Perez puts so much emphasis on such obvious red herrings.

With respect to Mr. Perez’s point that the crowding out hypothesis is not surprising at all (since “renewables and nuclear power are structurally incompatible, so there is an anti-correlation between them”), we would note that he is directly endorsing (without duly emphasized acknowledgement) one of the most crucial findings of our paper.

And to be clear, we do not emphasize mere theoretical properties of random variables, which need opaque assumptions and are devoid of empirical data. That Mr. Perez states on such an ostensibly precise theoretical basis, “little to no surprise”, detracts from his idiom of precision. It raises the question: is it really no surprise or is there something to be investigated? It is the empirical findings we obtain – together with our qualifications – that strike us without doubt as being something to be investigated.

With respect to the important role played by hydroelectricity in the earlier period we examine, Perez again deploys a misleading polemic. Why should this unavoidable empirical reality be treated as if it were somehow a deficiency of our study? The relative importance of hydroelectricity in the early stages of renewables uptake is simply a reflection of the established historical trajectory in renewable development. In later stages, the effects we document in this regard become much more influenced by wind and solar. With all these sources anyhow counting as ‘renewable’, why would this count as a ‘flaw’.

With respect to timeframes, the question raised is (as we acknowledge) about nuanced differences of approach, not about “mistakes”. We are ourselves clear that there are multiple things to consider on this issue. This is exactly why we have chosen a robust data-averaging approach with several triangulation procedures. Together with our openness to the many conditionalities, this is the way to properly address uncertainties and ambiguities that are unavoidable in this kind of research. If Mr. Perez really wants to claim that there exists just one single definitive approach to this complexity, then he is arguably reproducing the kind of technocratic authoritarianism that has led for so long to the neglect of the kinds of questions we are raising.

As to Mr. Perez’s argument that “you do not have stationarity” and “you need stationarity for time series analysis”, we agree. But this is again a strangely misleading point. It is this need for stationarity in time series approaches that constitutes a key reason why we do not adopt such an approach.

Here, the argument that we should have used panel data and that our analysis is unduly time-averaged actually go together. While panel data analysis may be an alternative, we intentionally chose time averaging since this procedure enables more robust statements to be made in the context of random variables (the underlying modelling for statistics). Such approaches are often used as a mean to average nuisance contributions in an environment with a presence of many influencing factors which clearly is our case at hand.

We choose the stated time lag without involvement of a second category of assumptions that would not compellingly fit the purpose of an initial pioneering study. As we explain, the indicated time lag was chosen to optimally use the data set. Otherwise, we might have disregarded precious data points which would then in turn have raised the objection that we intentionally and deliberately used only some parts of the data, but not all of it, thereby wasting parts of the available data set. Crucial here, is that we still have to consider a directional effect since power plants typically involve a lot of down-stream processes (such as maintenance) that stretch over time but need to be clearly attributed.

To Mr. Perez’s statement that “distribution of the residuals is not exactly normal”, we respond that any expert should be aware that any test of assumptions only gives hints for acceptance within defined error intervals. When invoking statistical pretests, all issues surrounding statistical tests, like “false positives”, power and efficiency of tests, have to be mentioned.

On a further technical point, Mr. Perez refers to “confounding variable with a power law and not just a linear model”. But there is no part of our work that relies on identifying a “best fit” curve. This would be difficult to motivate from a theoretical perspective – for example: why squared or the root. We are not aiming to build a “causal model”. We never claim to do so. Why does Perez imply otherwise?

In similar vein, Mr. Perez makes statements about the “predictive power” of our model that compound a further diversion with a misquote. What we actually said was “Crucially, renewable energy strategies are, to an evidently noteworthy degree, associated with lower levels of national carbon emissions”. This is not an attribution of causality. Whether one might have chosen a different analytical approach is a moot point that we acknowledge. But all methods hold pros and cons. Oddly for someone so focused on precision, Mr. Perez does not demonstrate that alternatives do not display their own more serious specific disadvantages.

With regard to Mr. Perez’s statement that the original data would yield a “bias”: we are not adjusting/distorting the original data, we analyze it simply it as it is. His slurs about “the poor study of the data set” and “suboptimal modeling” can be qualified in light of our response to his other misleading language addressed above.

Multivariate linear regression is actually quite robust with respect to its assumptions. What is most crucial here is that it was not our aim in this pioneering study to test any particular model versus another as a candidate for an “optimal fit”. What we are instead doing, is investigating prevailing understandings of the form “the more … energy, the less emissions”. So our methodology stands in this regard.  Given that the associated issues are so prominent and so high stakes, it is remarkable that our research question has not been posed before.

In conclusion, we would urge that the reader cut through the many technicalities to see the underlying picture. Our study asks a very basic empirical question. We do not claim to have answered this definitively, but merely pointed to the significant implications and the grounds for further research. Our findings remain valid and salient.


Response to Sovacool et al.’s response

Daniel Perez

Benjamin K Sovacool et al.: We thank Mr Perez for engaging with our article. But we do not believe any of the concerns he raises are novel, nor do they hit the point on many aspects.

First of all, if he had read thoroughly, Mr Perez might have noticed that we never talk about greenhouse gases in their full generality. To say we use GDP to “confound something” is a serious misrepresentation. We actually use GDP as a “control”.

Mr Perez also seems to misunderstand us when he says: “despite the fact that decarbonated energy sources are not good predictors of GHG emissions” and “Fossil fuels as the real predictor and the ‘crowding out’ hypothesis”. Just as we never address GHG in their entirety, so we never claim that clean energy sources are a “predictor” of CO2 emissions. Ours is not a predictive but a correlative study. The reader might wonder why Mr Perez puts so much emphasis on such obvious red herrings.

Daniel Perez: The terms “confounding variable” and “predictors” are widespread and well-known concepts in statistics. Both of these terms are standard terminology in the context of regression analysis, as can be corroborated by looking at any statistics textbook. It’s in the statistical sense that the correlative study made in Sovacool et al.’s paper explicitly uses both nuclear and renewables as predictors of GHG emissions.

Sovacool et al.: With respect to Mr Perez’s point that the crowding out hypothesis is not surprising at all (since “renewables and nuclear power are structurally incompatible, so there is an anti-correlation between them”), we would note that he is directly endorsing (without duly emphasized acknowledgement) one of the most crucial findings of our paper.

Perez: This is a misquote, we were simply explaining Sovacool et al.’s reasoning. The full statement reads as follows: “Moreover, the reasoning behind the “crowding out” hypothesis is flawed. Indeed, the authors of [16] motivate the proposal of the “crowding out” hypothesis as follows: Intermittent renewables require a decentralized electrical infrastructure as soon as they occupy a significant fraction of the electricity produced. By contrast, the optimal electrical infrastructure of non-intermittent power sources, such as fossil fuels, hydroelectricity and nuclear power is centralized [2]. The authors then suggest that, for these reasons, there should be an anticorrelation between R and N, which is the statement of the so-called “crowding out” hypothesis. They back this statement by verifying that R and N are indeed anticorrelated and use this to justify their statements.” It is clear that nowhere are we agreeing with their conclusions, but rather just explaining the reasoning proposed by Sovacool et al. as to their proposal of the “crowding out” hypothesis.

Sovacool et al.: And to be clear, we do not emphasize mere theoretical properties of random variables, which need opaque assumptions and are devoid of empirical data. That Mr Perez states on such an ostensibly precise theoretical basis, “little to no surprise”, detracts from his idiom of precision. It raises the question: is it really no surprise or is there something to be investigated? It is the empirical findings we obtain – together with our qualifications – that strike us without doubt as being something to be investigated.

Perez: Whether Sovacool et al. were aware of their emphasis on a phenomenon arising when studying fractions of a same whole in a regression analysis is irrelevant in the demonstration that their “findings” are mere artefacts of this fact, as clearly demonstrated in our work.

Sovacool et al.: With respect to the important role played by hydroelectricity in the earlier period we examine, Perez again deploys a misleading polemic. Why should this unavoidable empirical reality be treated as if it were somehow a deficiency of our study? The relative importance of hydroelectricity in the early stages of renewables uptake is simply a reflection of the established historical trajectory in renewable development. In later stages, the effects we document in this regard become much more influenced by wind and solar. With all these sources anyhow counting as ‘renewable’, why would this count as a ‘flaw’.

Perez: That “the effects in this regard become much more influenced by wind and solar” remains to be shown, as hydroelectricity accounts for a much higher percentage of energy produced world-wide than both of these sources of energy combined, particularly so in both the timeframes considered by Sovacool et al. To extrapolate their findings to a regime where solar and wind power were to become dominant deserves at the very least a justification, which is not present in their paper. Let us stress that, although hydro, wind and solar share the renewable characteristics, the large uncontrolled variability of wind and solar production make them very different to hydroelectricity in that respect.

Sovacool et al.: With respect to timeframes, the question raised is (as we acknowledge) about nuanced differences of approach, not about “mistakes”. We are ourselves clear that there are multiple things to consider on this issue. This is exactly why we have chosen a robust data-averaging approach with several triangulation procedures. Together with our openness to the many conditionalities, this is the way to properly address uncertainties and ambiguities that are unavoidable in this kind of research. If Mr Perez really wants to claim that there exists just one single definitive approach to this complexity, then he is arguably reproducing the kind of technocratic authoritarianism that has led for so long to the neglect of the kinds of questions we are raising.

As to Mr Perez’s argument that “you do not have stationarity” and “you need stationarity for time series analysis”, we agree. But this is again a strangely misleading point. It is this need for stationarity in time series approaches that constitutes a key reason why we do not adopt such an approach.

Here, the argument that we should have used panel data and that our analysis is unduly time-averaged actually go together. While panel data analysis may be an alternative, we intentionally chose time averaging since this procedure enables more robust statements to be made in the context of random variables (the underlying modelling for statistics). Such approaches are often used as a mean to average nuisance contributions in an environment with a presence of many influencing factors which clearly is our case at hand. We choose the stated time lag without involvement of a second category of assumptions that would not compellingly fit the purpose of an initial pioneering study. As we explain, the indicated time lag was chosen to optimally use the data set. Otherwise, we might have disregarded precious data points which would then in turn have raised the objection that we intentionally and deliberately used only some parts of the data, but not all of it, thereby wasting parts of the available data set. Crucial here, is that we still have to consider a directional effect since power plants typically involve a lot of down-stream processes (such as maintenance) that stretch over time but need to be clearly attributed.

Perez: Non-stationarity is an important phenomenon in this particular timeframe, as many countries underwent rapid development in the 90s and the 00s. It was never claimed in our paper that “time series require stationarity”, which is a false statement. Time series analysis can be performed even in a non-stationary setting, for instance by using a Moving Average (MA) or Moving Average Exogenous (MAX) model, which was not the case in Sovacool et al.’s work. Other standard tools in this context are Autoregressive processes (ARPs) or Autoregressive exogenous processes (ARXs). All of these tools are well-adapted to indeed study whether the claims of Sovacool et al. regarding the nature of the time lag are justified or not. However, non-stationarity in particular implies that considering only two-time steps with an a priori arbitrary lag is an incorrect approach from a statistical point of view. The averaging chosen by the authors is not justified from a time-series analysis perspective and does not exploit the data in any sense of optimality (from a statistical standpoint). That this procedure is “robust” remains to be shown by, for instance, demonstrating its stability, i.e. whether a change in the time step and number of timeframes considered changes the conclusions of the regression analysis or not. This was never made explicit by the authors in their paper. Furthermore, whether their justification for the lag is correct or not also would require a finer time series analysis. Regardless, this was not the main argument provided in our paper, although we point out that more adequate tools exist for treating the data. As we stated in our paper, even taking the averaged-out data from Sovacool et al. there are many other problems regarding their analysis, which are not related to these time series considerations.

Sovacool et al.: To Mr Perez’s statement that “distribution of the residuals is not exactly normal”, we respond that any expert should be aware that any test of assumptions only gives hints for acceptance within defined error intervals. When invoking statistical pretests, all issues surrounding statistical tests, like “false positives”, power and efficiency of tests, have to be mentioned.

Perez: The only time we make this remark is when we are reporting the t-statistic, standard error of our regressions and their p-value. We remark that looking at p-values is irrelevant here, and that the standard error and t-statistics are thus the relevant metrics to look at.

Sovacool et al.: On a further technical point, Mr Perez refers to “confounding variable with a power law and not just a linear model”. But there is no part of our work that relies on identifying a “best fit” curve. This would be difficult to motivate from a theoretical perspective – for example: why squared or the root.

Perez: The full quote is “despite the fact that going forwards we should consider accounting for the confounding variable with a power law and not just a linear model.” As is clear from inspection of the data in a log-log chart, the GDP vs CO2eq emissions are better described by a power law rather than just a linear model, as was shown in our analysis. That this should be the case is not necessarily a surprise, as the data is clearly heteroskedastic and spans many orders of magnitude. This is often a sign that the underlying distribution should be Pareto, hence our inspection of whether this hypothesis holds or not.

Sovacool et al.: We are not aiming to build a “causal model”. We never claim to do so. Why does Perez imply otherwise?

Perez: On this point, let us quote the authors on the conclusions of their paper: “When taken together with the finding that renewables seem significantly more positive for carbon abatement, important adverse implications arise for nuclear power. As the evidently less generally favourable of the two broad carbon emissions abatement strategies, a tendency of nuclear not to coexist well with its renewable alternative, does (all else being equal) raise doubts about the opportunity costs of investments in nuclear power rather than renewable energy. The direction of cost and learning trends discussed here, intensify this point. Given the current state of climate debates internationally and in many countries, it is troubling that nuclear and renewable energy pathways appear (both historically and, here, empirically) to display such mutual tension. It appears that countries planning large-scale investments in new nuclear power are risking suppression of greater climate benefits from alternative renewable energy investments. That the converse may also be true (with renewables tending to suppress nuclear investments) is evidently less important, because it is renewable strategies that are on balance evidently more effective at carbon emissions mitigation.” If the authors did not seek to exploit a causal model in which the link between the variables studied was properly understood, drawing such conclusions from a simple correlation study exhibiting the several statistical caveats mentioned in our paper is at best unjustified. Alternatively, if the objective of the paper was to make policy recommendations, then the study of a causal model becomes necessary (albeit, not necessarily sufficient).

Sovacool et al.: In similar vein, Mr Perez makes statements about the “predictive power” of our model that compound a further diversion with a misquote. What we actually said was “Crucially, renewable energy strategies are, to an evidently noteworthy degree, associated with lower levels of national carbon emissions”. This is not an attribution of causality.

Perez: Once again, “predictive power” is a common expression in the statistical jargon typically used in regression analysis.

Sovacool et al.: Whether one might have chosen a different analytical approach is a moot point that we acknowledge. But all methods hold pros and cons. Oddly for someone so focused on precision, Mr Perez does not demonstrate that alternatives do not display their own more serious specific disadvantages.

Perez: The matter is not whether a particular method holds pros or cons, but rather to point out that there are many methodological mistakes in the analysis in Sovacool et al.’s paper. For example, performing correlations over fractions of the same whole, disregarding that data concerning nuclear power necessarily has a considerably smaller variance than that of renewables, by circumstance (lots of countries have little to no nuclear power, whereas there are very few countries with a large portion of nuclear in their electrical mix). It is not a matter of a pro or con but simply a methodological mistake. Finally, we are explicit in our paper in stating that our goal was to reproduce the study of Sovacool et al. not to do our own on the same subject.

Sovacool et al.: With regard to Mr Perez’s statement that the original data would yield a “bias”: we are not adjusting/distorting the original data, we analyse it simply it as it is. His slurs about “the poor study of the data set” and “suboptimal modelling” can be qualified in light of our response to his other misleading language addressed above.

Perez: cf. our previous discussion on time series analysis considerations.

Sovacool et al.: Multivariate linear regression is actually quite robust with respect to its assumptions. What is most crucial here is that it was not our aim in this pioneering study to test any particular model versus another as a candidate for an “optimal fit”. What we are instead doing, is investigating prevailing understandings of the form “the more … energy, the less emissions”.

Perez: Precisely, and what we show in our paper is that it is not possible to conclude, using the methodology of Sovacool et al., anything other than “fossil fuels emit CO2”.

Sovacool et al.:  So our methodology stands in this regard. Given that the associated issues are so prominent and so high stakes, it is remarkable that our research question has not been posed before. In conclusion, we would urge that the reader cut through the many technicalities to see the underlying picture. Our study asks a very basic empirical question. We do not claim to have answered this definitively, but merely pointed to the significant implications and the grounds for further research. Our findings remain valid and salient.


Having considered the points Mr Perez raises, Prof. Sovacool, Prof. Stirling and their co-authors feel they have been adequately covered in their initial response.

Profs Sovacool and Stirling will share further reflections on the response to the paper and the challenge of maintaining open debate in energy debates in another blog post, to follow shortly on this site.

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Sussex Energy Group new projects: IDRIC, CREDS & more

Power plant image

The UK Industrial Decarbonisation Research and Innovation Centre (IDRIC)

The decarbonisation of industrial clusters is of critical importance to the UK’s ambitions of cutting greenhouse gas emissions to net zero by 2050. The UK Industrial Decarbonisation Challenge (IDC) of the Industrial Strategy Challenge Fund (ISCF) aims to establish the world’s first net-zero carbon industrial cluster by 2040 and four low-carbon clusters by 2030.

The vision of IDRIC is to become a world-leading, high-impact research and innovation centre, acting as the national focal point and international gateway for UK industrial decarbonisation research and innovation.

SEG Director Benjamin K Sovacool is co-director of the Social, Economics, and Policy Research Innovation Theme. This theme examines social attitudes, preferences, and the sociotechnical dynamics of industrial decarbonisation. Read the University of Sussex IDRIC launch press release.

IDRIC’s strategic objectives are to: accelerate challenge-led research through transformative innovation; develop leadership by nurturing talent, building capacity and mapping skills; co-create and share knowledge by stimulating cross-learning, active networks and outreach; support policy and mission advocacy by providing evidence to policy makers and the public.

IDRIC is backed by £20m funding until 2024. The initiative is part of the £170m Industrial Decarbonisation challenge, delivered through the UKRI Industrial Decarbonisation Challenge.

CREDS: Digital Twin project

This Centre for Research Into Energy Demand Solutions (CREDS) project, starting in September 2021, aims to examine the development of the digital twin (DT) concept within the built environment sector. It will investigate the drivers and barriers for DTs to transform understanding and practices to reduce energy demand.

The DT concept is increasingly prominent in the built environment and infrastructure sectors in the UK. A DT is a virtual replica of a physical asset or system that uses system data to provide a representation of it in operation; the technique has been used on equipment like jet engines and power generation turbines. Its application for systems within the built environment presents potentially powerful tools for both system and policy innovations to shape and reduce energy demand.

The project aims to answer the following questions:

  • How can the development and use of DTs in the built environment connect with understandings of energy demand to facilitate transformation for net zero?
  • Where and how is the DT concept being developed within the built environment sector?
  • What are the drivers and barriers to incorporating energy demand understanding within DT tools and their use?
  • What opportunities exist (or should be generated) to connect DT tools to energy demand understanding and transformation for net zero?

CREDS: Place-based business models for net-zero

This project will investigate how place-based business models for net-zero are being developed through digital living/working since the global Covid-19 pandemic, and how these place-based business models reduce energy demand.

To do this, the project will examine three prominent and emerging areas for net-zero action in Sussex and beyond involving: i) new opportunities for financing net-zero projects; ii) nature-based solutions for net-zero; iii) capacity building for decarbonisation skills.

The project will seek to study the ways in which these three areas have emerged through digital living, working and connections.

For example, the pandemic response encouraged people to minimise travelling and work from home, producing increased engagement and interest in nature-based solutions. However, the question remains how these fit with other, more technically oriented, net-zero-driven business model in Sussex.

The project also seeks to directly investigate how these three factors shape place-based opportunities for energy demand reduction, a gap which could lead to powerful asymmetries in the inclusion of energy demand action in developing place-based net-zero responses.

Research questions the project aims to answer include:

  • How do digital living, connections and working shape the emergence of place-based business models for net-zero?
  • How do emergent place-based business models shape opportunities for energy demand reduction?
  • What are the financing options and opportunities for projects/action on net-zero?
  • How is capacity building for decarbonisation skills for net-zero developed?
  • What are the nature-based solutions for net-zero?

Democratising the Just Transition: the role of Community Wealth Building

Dr Max Lacey-Barnacle has recently been awarded the Leverhulme Trust’s Early Career Research Fellowship, which he will begin in October this year, with supervisory support from Professor Tim Foxon.

Democratising the Just Transition: the role of Community Wealth Building will seek to draw upon principles of Community Wealth Building to understand how to democratise the forthcoming transition to a net-zero economy in a way that diversifies ownership and reorients a green recovery towards local economies and supply chains.

Using a mixed methods approach, the fellowship will draw on three contexts (Europe, the US and the UK) and in-depth interviews to synthesise cross-national insights on democratic pathways to a Just Transition. The fellowship’s outcomes will include publication in high-impact journals, collaboration with policy practitioners and an international research visit for cross-institutional knowledge exchange.

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Long Run Trends in ICT Demand and its Impact on Energy Consumption

Phone screen being held in one hand

This article was written by Dr Roger Fouquet, Associate Professorial Research Fellow at the Grantham Research Institute on Climate Change and the Environment, on his CREDS Digital Society project.

Understanding ICT Long Run Trends

Social distancing rules associated with Covid-19 have led many in the UK and across the world to work and meet remotely, as well as shop online. In other words, there was an acceleration of the digitalisation of society that has already been underway for several decades. The purpose of the CREDS Digital Society project led by Dr Roger Fouquet at the London School of Economics and Political Science (LSE)’s Grantham Research Institute on Climate Change and the Environment is to place the digitalisation of society within a longer run perspective of information and communication technology (ICT) demand and its impact on energy consumption, with a view to anticipating future trends.

The project collected data on the price and consumption of communication for the UK. Figure 1 shows the dramatic increase in communication in the UK over the last 150 years, as new technologies appeared on the market and diffused across the population. One of the key factors driving this rise in consumption was the reductions in the cost of sending a letter or making a telephone call (see Figure 2). Indeed, today, email and app mobile phone calls are effectively free for many people.  

The Benefits to Users from New Communication Technologies

Despite the upward long-run trend in communication since the mid-nineteenth century, Figure 1 shows the acceleration in communication use from the beginning of the twenty-first century. The first task of the project was to use the data to estimate how communication consumption changed with variations in income and communication prices. One of the main results is that as people became richer and communicated more, they were less responsive to changes in income and prices – a similar observation was found in relation to energy services, such as heating, transport and lighting (Fouquet 2018).

This information on consumer responsiveness then enabled the estimation of the net benefits (i.e., consumer surplus) of different communication technologies, using a method developed in Fouquet (2018). This method uses historical information about how much people in the past were willing to pay for communication (e.g., to send one letter or make one phone call) and, taking account of changes in income, extrapolates this forward towards the present to construct a full demand curve for communication. The net benefits (i.e., consumer surplus) are calculated as the difference between the benefits (measured by the willingness-to-pay) and the costs (measured by the price).

Figure 3 reveals the increases in net benefits to consumers from new and diffusing communication technologies. This started with the reduction in the price of postal services due to the railways in the 1840s (seen in Figure 2). Then, with the democratisation of telephones in second-half of the twentieth century and the liberalisation of telecommunication services leading to lower prices in the 1980s, benefits increased again. Figure 3 also highlights that some of the benefits are simple substitutions of fixed telephones for mobile phones and letters for emails. Nevertheless, the digitalisation of communication has helped increase consumer surplus even more, especially because they are so cheap to use.

Taking Account of the Environment

An important angle for future research is to understand how this digitalisation of communication and information and its acceleration due to Covid-19 will impact society and the environment. Indeed, on the one hand, teleworking, virtual meetings and online shopping have reduced energy use and emissions (Hook et al. 2020). On the other hand, digitalisation, especially associated with data centres, is responsible for increasing energy use (Koomey et al. 2013). Thus, a crucial question to be explored further will be to compare the benefits to the consumer with the costs to the environment, and understand when consumers benefit more than the cost to the environment, and when policies should discourage ICT use because the costs dominate.


Figure 1. The Consumption of Communication in the UK, 1800-2015
Figure 2. The Price of Communication in the UK, 1700-2015
Figure 3. Consumer Surplus from Communication Technologies and Services in the UK, 1830-2010

References

Fouquet, R. (2018) ‘Consumer surplus from energy transitions.’ The Energy Journal 39(3) 167-88.

Fouquet, R. and Hippe, R. (2019) ‘The Transition from a Fossil-Fuel Economy to a Knowledge Economy’ in Fouquet, R. (ed.) Handbook on Green Growth. Edward Elgar Publications. Cheltenham, UK, and Northampton, MA, USA.

Fouquet, R. and Hippe, R. (2021) The Twin Transition: Energy and Communication in the Structural Transformation of European Economies.

Hook, A., Court, V., Sovacool, B.K. and Sorrell, S. (2020) ‘systematic review of the energy and climate impacts of teleworking.’ IFP School-IFPEN Working Paper. No 133.

Koomey, J.G., Matthews, H.S. and Williams, E. (2013) ‘Smart Everything: Will Intelligent Systems Reduce Resource Use?’ Annual Review of Environment and Resources 38: 311–43.

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