1
ol.:ȋͬͭͮͯͰͱͲͳʹ͵Ȍ
Scientific Reports
| (2022) 12:19017
|
https://doi.org/10.1038/s41598-022-23624-9
www.nature.com/scientificreports
Social media enables
people‑centric climate action
in the hard‑to‑decarbonise building
sector
Ramit Debnath
1,2
*
, Ronita Bardhan
1
*
, Darshil U. Shah
1
, Kamiar Mohaddes
1
,
Michael H. Ramage
1
, R. Michael
Alvarez
2
& Benjamin K.
Sovacool
3,4,5
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. Between
2009–2012, discussions around green building‑led emission reduction efforts were highly influential
in shaping the online public perceptions of climate action. From 2013 to 2016, communication
around low‑carbon construction and energy efficiency significantly influenced the online narrative.
More significant interactions on net
‑zero transition, climate tech, circular economy, mass timber
housing and climate justice in 2017–2021 shaped the online climate action discourse. We find positive
sentiments are more prominent and recurrent and comprise a larger share of the social media
conversation. However, we also see a rise in negative sentiment by 30–40% following popular policy
events like the IPCC report launches, the Paris Agreement and the EU Green Deal. With greater online
engagement and information diffusion, social and environmental justice topics emerge in the online
discourse. Continuing such shifts in online climate discourse is pivotal to a more just and people‑
centric transition in such hard‑to‑decarbonise sectors.
The Intergovernmental Panel on Climate Change (IPCC) suggests that restricting climate change to 1.5
◦
C
requires rapid and extensive changes around energy use, building design, and broader planning of cities and
infrastructure
1
. The buildings and construction sector currently accounts for around 39% of global energy and
process-related carbon
emissions
2
,
3
. The International Energy Agency estimates that to achieve a net-zero carbon
building stock by 2050, direct building carbon emissions must decrease by 50%, and indirect building sector
emissions must also decrease 60% by
2030
4
. In a global call for net-zero strategies, a collaboration between the
UN High-Level Climate Champions, the COP26 Presidency, the UK’s Department for Business, Energy and
Industrial Strategy (BEIS) and the #BuildingToCOP26 Coalition announced 26 built environment climate initia-
tives at the Cities, Regions and Built Environment Day at the UN
COP26
5
. It included net-zero carbon building
commitments of over USD 1.2 trillion by the World Green Building
Council
6
, and a Race to Zero through the
C40 Cities Clean Construction Action Coalition including over 1049 cities and local governments, representing
roughly 722 million people and committed to reducing 1.4 gigatons of CO2 equivalent by
2030
7
.
A growing body of evidence from the stakeholder community emphasises the need to incorporate public
voices in global climate action to enable an equitable and just
transition
8
–
13
. As all climate solutions will involve
people one way or another, there should be a greater emphasis on socio-technical solutions and the social sci
-
ences, in addition to the continued development of complex technical
solutions
14
. The European Union and the
White House have also emphasised the need to create a democratised space for involving citizens at various levels
of decision-making
10
,
11
,
13
. However, enabling democratic participation of people in the decarbonisation process
remains a critical challenge across the local, national and regional
scales
15
–
17
. Decarbonising the building sector
OPEN
1
University of Cambridge, Cambridge
CB2 1TN, United Kingdom.
2
California Institute of Technology, Pasadena,
CA 91125, United States.
3
Boston University, Boston, MA
02215, United States.
4
University of Sussex Business
School, Brighton
BN1 9SN, United Kingdom.
5
Aarhus University, Aarhus
8000, Denmark.
*
email: rd545@
cam.ac.uk; rb867@cam.ac.uk
2
Vol:.(1234567890)
Scientific Reports
| (2022) 12:19017 |
https://doi.org/10.1038/s41598-022-23624-9
www.nature.com/scientificreports/
is challenging as it involves a complex overlap of people, places and practices that creates a barrier to designing
just emission reduction
policies
18
–
20
.
In addition, the distinctive socio-demographic and bio-physical contexts of the built environment makes
it tremendously resource intensive to use traditional survey instruments at
scale
21
,
22
. However, the emergence
of new data sources like time-series social media interaction datasets has opened up new possibilities for the
large-scale cross-sectional study of such complex
systems
23
–
25
. In this study, we use English-language social
media (Twitter) data over 13 years (2009–2021, n = 256,717 tweets) to examine public reactions to climate policy
events concerning building sector emissions reduction (i.e. April 2009 (before COP15) to November 2021 (after
COP26)). The scale of the climate events is global and organised by intergovernmental bodies like the United
Nations Framework Convention on Climate Change (UNFCCC) and IPCC.
In doing so, we look at the complex dialectic relationship between social media and climate change politics/
policymaking that may shape user opinion and
reactions
26
. As a result of social media, citizen journalism has
increased the immediacy of breaking news; this has accelerated the speed at which politics is conducted and
perceived
27
,
28
. With over 4.26 billion social media users
worldwide
29
, the boundaries between local and global
“content” has been blurred, which is increasingly seen as a critical co-production factor for climate
action
30
. In
this paper, we leverage this new form of digital data to capture cross-sectional variation in public sentiments
and emotions following global emission reduction events concerning the building sector, thereby creating new
knowledge for evaluating a people-centric and just transition with their emotional responses to climate policy
processes. Our framing of “reactiveness” is based on the simultaneous consumptive and expressive characteristics
of social media that make an individual’s news feeds highly
personalized
25
,
31
. However, echo chambers exist in
social media, in which individuals cluster among like-minded
individuals
32
,
33
.
Scholars agree that while personalised news feeds contextualise relevant issues (social, political, economic,
etc.), they are also a useful lens for analysing climate change
opinion
25
,
34
. They also provide a setting to examine
what sentiment users have about climate
change
35
,
36
and the ability to analyse how people frame their issues in
the
discussion
25
,
36
. In an ontological context, evidence shows that social media contains a greater spectrum of
non-elite (general public generated content) social conversations which are not part of mainstream media organi-
sations. However, these occur alongside elite (politicians, advocacy groups, etc.) conversations, providing a much
larger intersectional bandwidth of information on public opinion which is critical for policy
evaluation
25
,
37
–
39
.
It is this information intersectionality that makes social media reactive to climate change content. For example,
studies have shown that Twitter engagement spikes and search volume peaks around specific news stories on
climate-induced weather events, high-profile media events like Al Gore’s and the IPCC’s nomination for the Noble
Peace
Prize
36
,
40
. Similarly, UNFCCC and IPCC events have shown to encourage higher hashtag use on
Twitter
40
.
We conceptualise a people-centric transition in the context of the built environment as a medium of design
thinking, i.e., to design interventions that are both effective in reducing embodied and operational emissions, as
well as achieving wider societal goals of environmental justice: wellbeing, equity and fairness. Recent research
have shown that social media platforms like Twitter can be used to derive causality discourse for users reactive-
ness to climate change-related
events
26
,
41
–
43
. Three factors are central to understanding causality discourse on
Twitter: the extreme-event factor, the media-driven science communication factor, and the digital-action
factor
26
(see SI Table
1
for detailed explanation). We operationalise this framework in this paper to evaluate three ques-
tions empirically: (i) What are the characteristics of a people-centric transition on social media towards emis
-
sion reduction in the building sector over the 13-year time frame?; (ii) How has this messaging been received
by users (i.e., public reactions) on social media over global climate negotiation and policy events on building
emission reductions?; and (iii) How have the critical discourses changed over the temporal scale in the context
of a people-centric transition?
The novelty of this paper is twofold. First, it empirically evaluates causality discourses of social media mes
-
saging of high-level policy events concerning emission reduction in the built environment. Second, it meth-
odologically expands the use of social media interaction data from Twitter to define people-centric transition
in the context of global climate action in the building sector. The methodology is a multi-method application of
natural language processing (NLP), sentiment analysis and network theory (see “
Methods
” section). Therefore,
we contribute significantly with our use of state-of-the-art computational social sciences applied to the domain
of just policy design. The findings from this study will be helpful to a wide range of stakeholders who are explor
-
ing pathways for a people-centric transition and its contextualised implementation of low-carbon strategies in
the context of a net-zero future.
We evaluate how different global climate action events shaped the discussions around emission reduction
and low-carbon transition of the built environment using hashtag co-occurrence networks. As mentioned above,
hashtags can be used to measure climate communications on
Twitter
25
,
40
, as two overlapping processes influence
the choice of using a particular hashtag: attention-seeking behaviour by users and the contagion process driven
by the virality of specific
hashtags
44
,
45
. Moreover, studies have also found that Twitter hashtags offer a strategic
vantage point on social movements as they provide scalability through networked information
dissemination
45
.
Berglez and Al-Safaq
26
theorise this as a causality discourse influenced by the digital-action factor, and we expand
on their theory to test how certain hashtags are propagated over time. These characteristics of hashtags motivated
our study to investigate how hashtag networks in the emission reduction in building discourse are shaped in the
people-centric transition of Twitter communication.
Towards people‑centric climate action in building sector emissions reduction
Strategies to enable a people-centric transition in the built environment are anecdotal and not always informed
by data. The proactive role of people in the decision-making process and the science-policy interface on climate
change are well-studied, for example, involving recent approaches like public-private-people-partnerships (4P),
3
Vol.:(0123456789)
Scientific Reports
| (2022) 12:19017 |
https://doi.org/10.1038/s41598-022-23624-9
www.nature.com/scientificreports/
value sharing governance, and information-to-empowerment
approaches
30
,
46
–
51
. However, very little academic
research is available on utilising public sentiments and emotions in just net-zero transitions in the building sector.
In a people-centric decarbonisation context, studies have shown that psycho-social factors like habits and
attitudes are strong determinants of individual
behaviour
52
. Studies like these have mentioned a habit-breaking
mechanism that could help reduce emissions in the mobility
sector
52
. It is also found that emotionally anchoring
and objectifying climate change in media communications can enhance public engagement in the issue and form
collective identities based on a mixture of
emotions
53
. Furthermore, a vast pool of evidence exists on behavioural
interventions for emissions reduction in buildings encompassing a range of initiatives like monetary incentives
involving financial rewards to nudges and non-monetary interventions information visualisation, feedback, and
social norms and
motivation
54
–
56
. However, these studies discuss interventions as a clinical measure to reduce
energy use and emissions in buildings without considering their user-emotional reaction. While capturing
such emotions on a large scale can be challenging and resource-intensive, Twitter data can provide a new way
of dynamically looking at how people react to climate action and policy decisions.
In addition, studies have shown that the individual-level expressions of environmental justice desires centre
around the notion that people are developing a shared future. Moreover, each individual feels they have some
-
thing to contribute in shaping, making and co-creating an equitable
future
57
. This conception aligns with notions
of personal responsibility and collective capability, extending Sen’s work on individual capabilities and collective
responsibility
58
. Individualistic capabilities influence a broad range of basic needs and functions like transporta-
tion, employment, health, housing, economic opportunities, community diaspora, and political participation
that shapes the collective meaning of environmental justice in the context of extreme weather
impacts
59
.
Kern and
Rogge
60
further demonstrated that if political, social and psychological dimensions can support eco-
nomic and technological innovations, the low-carbon transition can be achieved faster. Moreover, Martiskainen
and Sovacool explore the emotions (including positive and negative feelings) associated with low-carbon energy
transitions, including those in the built environment, and depict a range of reactions from joy and pride to fear
and
anger
61
. Similarly, Sovacool and Griffiths look at some of the negative cultural implications of building ret
-
rofits, showing how demographic aspects such as class or heritage dissuade households from pursuing energy-
efficiency
upgrades
62
. However, these studies approach the decarbonisation of the built environment through
the transportation and mobility sector.
In a similar methodological approach, another way to study public opinion on climate policy is through social
media, using data from Twitter, Facebook, YouTube, Reddit, and other social media
platforms
63
. There is a grow-
ing interest in using social media to examine human behaviour, attitudes, and interactions on a real-time, large
scale, within a short period, as opposed to conventional surveys or interview
methods
64
,
65
. In addition, social
media offers a unique platform where users can share their personal stories providing first-hand unmediated
accounts of their lived experiences. Studies have also found that social media can lead to greater empathy with
vulnerable groups and heightened response to the impacts of climate change or natural disasters while strength-
ening existing groups’ social ties by facilitating acts of caring, giving, and pro-social
behaviour
66
. For example,
a recent study explored tweets on the carbon emission trading system for multi-dimensional policy analysis in
the European Union (EU). It demonstrated the importance of the public’s cognition of climate
policies
67
. Fur
-
thermore, the study found that enabling public engagement (or people-centrism) in climate mitigation measures
allows people to express their environmental interests, improves the transparency of policy governance and
creates a space for the legitimacy of climate
policies
67
,
68
. Kirilenko, Molodostova and
Stepchenkova
42
found that
the public recognised extreme temperature anomalies and connected these anomalies to climate change through
Twitter use. Similarly, Yeo, S. L et al.
43
showed how the hashtags #globalwarming and #climatechange on Twitter
influenced lay audiences’ perceptions of climate change which have important implications for climate action
communication and discourse.
Also, Twitter data was used by Kim et al.
69
to examine the public’s emotional attitudes towards nuclear energy
as a low-carbon strategy. Sluban et al.
70
used hashtag networks to explore general emotional tendencies towards
’green energy’, ’climate change’ and ’carbon emissions’. That same study concluded that more public opinion
research is needed to enable a people-centric just transition. Tweets related to energy-related topics from the
EU Sustainability Energy Week were used to map stakeholders’ significant energy concerns and emotional
tendencies towards these issues by Bain and
Chaban
71
. Veltri and
Atanasova
72
explored the network topology
of climate change tweets and news media articles for automated text classification according to psychological
process categories. Recently, Twitter posts that mentioned climate change in the context of three high-magnitude
extreme weather events - Hurricane Irene, Hurricane Sandy and Snowstorm Jonas were used by Roxburgh et al.
73
to derive discourses of climate denialism, criticism and polarising political ideologies. An unsolicited public
opinion poll on climate change sentiments by Cody et al.
74
used Tweets between 2008 and 2014 to explore the
public emotional response to natural disasters, climate bills and oil drilling. Similarly, Debnath et al.
75
have used
Facebook posts to explore public perceptions of climate technology (in this case, electric vehicle) adoption across
political, economic, social, technological, legal and environmental policy dimensions.
However, none of the above studies explores the people-centric dimensions of building emission reduction
and its association with policy-led climate action. This research gap provides the primary motivation for our
study. We discover five salient findings, as discussed below.
Results
Public sentiments in emissions reduction in buildings: Five findings.
We begin by showing in
Fig.
1
the results from the sentiment analysis of the tweets containing #emission and #building between 2009 and
2021 (n = 256,717, see "
Methods
" Section). Five key findings arise. First, there is a strong relationship between
Twitter activity concerning the building sector and major policy events on climate change. The tweets are traced
4
Vol:.(1234567890)
Scientific Reports
| (2022) 12:19017 |
https://doi.org/10.1038/s41598-022-23624-9
www.nature.com/scientificreports/
Figure 1.
Time series of Twitter reactions concerning major climate negotiations and policy events (2009–
2021). (
a
) Twitter interactions (tweets, retweets, comments) with #emission and #building; (
b
) 6-month moving
average estimates of tweet sentiments; (
c
) Spearman correlation between negative sentiments with daily tweet
volume. The adjusted R-squared value is 0.296, standard error is 0.192 significant at 0.001 level, (
d
) Spearman
correlation between positive sentiments with daily tweet volume. The adjusted R-squared value is 0.299, and the
standard error is 0.271, significant at the 0.001 level.
5
Vol.:(0123456789)
Scientific Reports
| (2022) 12:19017 |
https://doi.org/10.1038/s41598-022-23624-9
www.nature.com/scientificreports/
as per major climate negotiations and policy events by the UNFCCC. For example, it can be seen in Fig.
1
a that
there have been topics relevant to building sector emissions in the IPCC reports, but it received greater engage-
ment following big report releases like the IPCC Special Report on Global Warming 1.5
◦
C
76
. A similar trend
in an exponential rise in Twitter engagement following a major climate communication event was also seen by
Berglez and Al-Safaq
26
, which was attributed to user network creation on social media platforms. Studies have
generally shown that Twitter engagement with the term ’emissions’ increases after extreme weather
events
25
,
77
.
However, our observation is uncommon for this hard-to-abate sector as we find people are also reactive to cli-
mate policy events.
Furthermore, using the causality discourse
lens
26
(which normatively relates climate communication on
Twitter and its associated user engagement), enables us to infer user reactiveness through sentiment analysis
following a high-level climate policy event. To use the causality discourse approach, we divided the 13-year
timeline into four temporal scales (N1 (2009–2012), N2 (2013–2016), N3 (2017–*2020) and N4 (2021)) in
order to evaluate Twitter causality discourses of external policy events. For N1 (2009–2013), it can be seen that
the appeal for global standards for reducing building emissions began soon after COP15 in 2009. Furthermore,
establishing the Green Climate Fund at the Cancun Agreement in 2010 encouraged the green building sector
to spotlight the discourse around built environment-centric emission reduction at COP17 in Durban (2011).
However, until COP18 in 2012, the focus was on energy efficiency and operational emission reduction benefits
through an extensive focus on green buildings (see Fig.
1
a).
Similarly, in the N2 (2013–2016) period (see SI Fig A1), policy discourse on integrated approaches to climate
change mitigation, adaptation and resilience of the built environment took centre stage with the release of the
IPCC Fifth Assessment Report on Climate Change (AR5) with a dedicated chapter on forecasting and long-range
planning for emissions reductions from the building sector
(see
78
and Fig.
1
a). This led to discussions on the need
for sustainable finance for low-carbon cities in COP-20 in 2014 (see Fig.
1
a). These shaped the Paris Agreement’s
critical messages for the building sector: reducing operational emissions through energy efficiency and addressing
the whole life cycle of the built environment sector (also mentioned
in
79
), and essentially flagged wide-ranging
policy discussions and stakeholder discourses on net-zero buildings and the built environment (see COP22 in
Fig.
1
a). Inferring from the causality discourse lens (see SI Table A1), it can be seen that both the popularity of
the policy events (Paris Agreement and IPCC AR5) and its extensive media-driven science communication led
to a greater engagement on Twitter.
Our results show the influence of a similar causality discourse around a higher Twitter engagement around
COP-23 (N4 (2017–2020)). This COP had a specific agenda called ’Human Settlement Day’, which focused on
cities, affordable housing and climate action. Such topical shifts also cause greater engagement within the users-
generated network, as seen through increases in retweets, following, and followers count during N4 (shown
in SI Figures A9, A10 and A11). Furthermore, with a similar causality lens, we found the tweet volume grew
exponentially with the launch of the IPCC Special Report on Global Warming of 1.5
◦
C, which stated the need
to enable more profound emission reduction in the urban and infrastructure
system
76
.
Moreover, the discourse on green/climate finance for residential homes got traction in COP25 in 2019, also
reflected in the ’circular economy’ and ’social housing fund’ discourses of the EU Green Deal (see Fig.
1
a and
80
).
The growing importance of emission reduction in buildings in the global climate action and policymaking
was further illustrated through the flagship ’Cities, Region and Built environment Day’ at the recent COP26 at
Glasglow (2021); and its correspondingly high Twitter traffic due to heavy media coverage and user engagement
driven by the digital media effect (defined as per the causality discourse lens, see SI Table A1) (see Fig.
1
a).
Second, the moving-average sentiment analysis shows that positive sentiments are more prominent and recur
-
rent and comprise a larger share of the social media conversation than negative sentiments, with few exceptions.
For example, the negative sentiment share rose to almost 40% from below 10% post-COP-15. However, this share
fell to nearly zero on the announcement of the Green Climate Fund (2010) (see Fig.
1
b). Similarly, tweets with
more than 50% negative sentiment peaked between COP-17 and COP-18 in June 2012 (see Fig.
1
b). In the same
period (i.e., N1: 2009–12), sentiment analysis found that tweets showed a more significant share of emotions
like ’trust’ and ’anticipation’, as shown in Fig.
2
(see SI Fig A1).
The sentiment trend for N2 (2013 - 2016) also shows a higher share of positive sentiment (cumulative share
of
≈
70%), with negative peaks during the Paris Agreement (
≈
50%, see Fig.
1
b). Between January and April 2013,
tweets showed emotions like high ’trust’, ’surprise’ and ’joy’, whose share fell significantly with the rise in negative
emotions like ’anger’ and ’fear’ in August 2013 (see Fig.
2
, SI Fig A1). The share for ’surprise’ increased during
COP-19. However, the critical key emotion shared during with IPCC AR5 release was ’anticipation’ (
≈
90%) and
’fear’ (
≈
60%, see Fig.
2
). A similar trend is seen in the tweets during the Paris Agreement, with an additional
share in ’trust’ (see Fig.
2
). Interestingly, the share of sadness increased after the IPCC Global Warming 1.5
◦
C
Report to
≈
30% (N3 and N4 (2018–2021), see Fig.
2
). Peaks in emotions like ’surprise’ and ’trust’ are also seen
post-EU Green Deal negotiations driven by a discourse that this new deal will kick start a building regulation
by replacing concrete with low-carbon materials like cross-laminated timber (see Fig.
2
). Thus, the sentiment
analysis showed public reactiveness in the building and emission to popular policy events, also observed
by
25
.
As a general result, we see that increases in Twitter engagement (i.e. daily Tweets) have a significant correlation
with both increases in negative (
R
2
=
0.296
at 99% CI) and positive sentiments (
R
2
=
0.299
at 99% CI) across
the 13-year timescale (see Fig.
1
c and d). Table
1
shows that the tweets with the highest negative sentiment scores
contribute to spikes in Fig.
1
b. Temporally an overlapping negative discourse is associated with a high carbon
tax and strict building codes. At the same time, hundreds of new coal power plants are being rapidly built. These
tweets also have geopolitical contexts, especially concerning China’s emissions reduction policies. More specific
to the building sector, tweets from the United States showed thematic associations with stakeholder groups (like
builders, utilities, and fossil fuel firms) lobbying against the implementation of climate-sensitive building codes