3.1. Is the transition to a low-carbon economy an opportunity or a risk?

Numerous studies have confirmed that climate change reduces well-being by negatively affecting the economy’s future potential growth and labour productivity and diverting resources from investment in productive capital to climate change adaptation (Burke et al., Reference Burke, Hsiang and Miguel2015; Burke and Emerick, Reference Burke and Emerick2016; Dafermos et al., Reference Dafermos, Nikolaidi and Galanis2018; IMF, 2018). Different regions and industries have different degrees of sensitivity to climate change (OECD, 2015; NGFS, 2018). Agriculture and carbon-intensive industries are often cited as the most affected due to both physical risks from climate events and transition risks from tightening climate change policies. However, financial institutions are also increasingly exposed to both categories of risks. If not anticipated, materialization of these risks may potentially lead to significant revaluation losses on financial assets (Dietz et al., Reference Dietz, Bowen, Dixon and Gradwell2016).

Climate change and the transition to a low-carbon economy can also provide opportunities. Tightening environmental regulations can induce efficiency and encourage innovations that help improve commercial competitiveness (Zeqiraj et al., Reference Zeqiraj, Sohag and Soytas2020). This is the ‘Porter Hypothesis’, which was formulated in the mid-1990s (Porter and van der Linde, Reference Porter and van der Linde1995). We are already seeing some sectors developing rapidly, such as research into new energy sources, carbon capture and storage systems, new types of energy storage systems, including batteries, and new types of building materials (for a detailed review, see, e.g., Napp et al. (Reference Napp, Hills, Soltani, Bosch and Mazur2017)).Footnote ⁷ The new technologies also provide an opportunity for financial market outreach and expansion. The funding of the transition to a low-carbon economy may partially compensate for the negative impact of the risks mentioned above.Footnote ⁸

In our questionnaire, we asked the respondents to evaluate whether the transition to a low-carbon economy is an opportunity or a risk (significant or some) for different financial and non-financial sectors (Figure 3). On average, the respondents recognize the transition to a low-carbon economy as an opportunity for the financial sector (both banks and non-banks, such as investment funds and insurance and pension companies) and agriculture, forestry and fishing. On the other hand, the respondents clearly recognize the transition as a risk for mining and quarrying. For the rest of the sectors, the respondents are fairly evenly divided between those recognizing opportunities and those recognizing risks, bringing the quantified mean response close to zero.

Figure 3.

Is the transition to a low-carbon economy an opportunity or a risk for the following sectors?

Notes: The left panel presents the percentage breakdown of all the respondents’ answers. The right panel shows the quantified mean responses of selected groups of respondents. The answers to this question were quantified as follows: significant opportunity (1); some opportunity (0.5); neither (0); some risk (−0.5); significant risk (−1); and no opinion (NA). We summarize the conversion of verbal answers to numerical ones for all questions in the appendix.

Regarding cross-sectional heterogeneity, the opinions are more or less consistent across the respondents’ locations. However, the opinions differ concerning the respondents’ professional roles. Specifically, respondents from central banks and those working in non-research positions are generally more pessimistic, recognizing more risks than opportunities across all sectors (light yellow symbols, right panel). On the other hand, respondents from universities and researchers are more optimistic, with their quantified mean response being more positive across all sectors. The difference between these two groups of respondents is statistically significant, especially for the financial sector, but to a lesser extent for other sectors as well. Another characteristic that plays a vital role is whether the respondent is considered to be environmentally conscious. Those categorized as more conscious are generally more optimistic, i.e., recognizing the transition as more of an opportunity than a risk across all sectors (dark blue symbols, right panel).

As an accompanying question to the previous one, we asked what impact the selected factors will have on the transition to a low-carbon economy in a given country or region (Figure 4). On average, most of the listed factors received high scores, identifying them as significant influences. The respondents attach the greatest weight to the industrial structure of the economy, a reliable national government, a sound institutional environment and enhanced international cooperation. They also rank highly the geographical location and overall environmental conditions and the ability of the private business sector to respond and adapt to changes quickly. On the other hand, they see a lower influence of households’ approach to their carbon footprint and financial system development, although not a negligible one. The heterogeneity among the respondents is driven primarily by their environmental consciousness, with more environmentally conscious individuals placing greater importance on the listed factors and less environmentally conscious respondents more frequently choosing options indicating some or no impact (dark vs. light blue symbols, right panel).Footnote ⁹

Figure 4.

What impact will the following factors have on the transition to a low-carbon economy in a given country or region?

Notes: The left panel presents the percentage breakdown of all the respondents’ answers. The right panel shows the quantified mean responses of selected groups of respondents. The answers to this question were quantified as follows: significant impact (1); some impact (0.5); no impact (−1); and no opinion (NA). We summarize the conversion of verbal answers to numerical ones for all questions in the appendix.

3.2. How important should the contributions and responsibility of different sectors be?

To be successful and timely, the transition to a low-carbon economy will require not only changes in relative prices and large-scale public and private investment in new technologies and practices, but also changes in consumer behaviour, production, land use and agricultural processes (IPCC, Reference Masson-Delmotte, Zhai, Pörtner, Roberts, Skea and Shukla2018). Fiscal policy naturally offers itself as one of the central policies for this transition, as it has a wide range of instruments at its disposal. Carbon pricing policies, such as carbon taxes and cap-and-trade systems, are critical to any successful climate mitigation strategy. However, the cost of carbon emissions is highly uncertain and extremely difficult to quantify (Gollier, Reference Gollier2012; Barnett et al., Reference Barnett, Brock and Hansen2020). Nevertheless, carbon taxes and emissions trading schemes seem to effectively reduce carbon emissions and generate significant revenues for the national budget (Farid et al., Reference Farid, Keen, Papaioannou, Parry, Pattillo and Ter-Martirosyan2016). Carbon pricing then motivates private firms to innovate more in clean technologies (Aghion et al., Reference Aghion, Dechezleprêtre, Hémous, Martin and van Reenen2016).

Financial flows play a vital role in transitioning to a low-carbon economy, as they drive technological transformations. Public investment is necessary, but private investment in production capacity, infrastructure, and research and development is crucial. Monetary and regulatory policy instruments can also play a role in promoting private climate finance. Some options are within most central bank mandates (consideration of climate risks in asset purchase programs or eligible collateral). In contrast, others may be more controversial (green asset purchases, credit allocation policies and adjustment of monetary policy frameworks). The existing literature offers little on the appropriate mix of climate mitigation policies. However, it points out that policy coordination will be crucial in this regard (Fay et al., Reference Fay, Hallegatte, Vogt-Schilb, Rozenberg, Narloch and Kerr2015).

Reflecting on this complex and vital issue, we asked our respondents how significant the contribution and responsibility of selected sectors – government, non-financial corporations, households, financial industry and central bank/financial regulator – should be in the transition to a low-carbon economy. Specifically, we asked them to rank the sectors from the most to the least important. In a follow-up question, we asked the respondents what were the most effective measures taken by the sector they ranked first. They were allowed to choose up to three listed measures (see the appendix for a detailed list).

The vast majority of the respondents believe that government should be the primary sector responsible for, and the main contributor to, the low-carbon transition (left panel of Figure 5). About 77 per cent of the respondents ranked government first, while only 13 per cent ranked it second, usually after non-financial corporations. Likewise, the respondents were consistent in their opinion of which government measure is the most effective in supporting the transition. More than 60 per cent of respondents stated that carbon taxes are the most effective. At the same time, an additional 40 per cent voted for cap-and-trade systems and regulatory policies for the non-financial sector, such as limits on car emissions. Only 18 per cent believe that enforcing disclosures of climate-related activities could be an effective measure, and only 8 per cent would opt for issuing sovereign green bonds. These views are relatively consistent across the respondents’ characteristics.

The second most important stakeholder contributing to mitigating the impact of climate change should be non-financial corporations, most respondents believe. About 45 per cent ranked this sector second after government, while an additional 28 per cent ranked it third, and 12 per cent ranked it first. Reducing firms’ carbon footprint and investing in innovation in climate technologies are considered the most effective measures taken by non-financial firms. As for the remaining stakeholders, households and the financial sector ranked, on average, third and fourth, respectively, while the central bank and the financial regulator ended up at the bottom of the list.Footnote ¹⁰ We do not see much heterogeneity among the respondents. Interestingly, respondents from central banks ranked the central bank lower and non-financial corporations higher than the other respondents.

As an accompanying question to the previous two, we asked what could be the barriers for the public and private sectors in the assessment and mitigation of environmental risks. We gave the respondents five options (right panel of Figure 5). These options received, on average, very similar ratings, with quantified mean responses ranging between 0.45 and 0.66. The respondents identified the lack of enforcement of and compliance with environmental risk standards as the most significant barrier, followed by vague regulation of environmental risks and misunderstanding and misrepresentation of environmental risks. Unavailability or low quality of data and untrained staff are perceived as less severe barriers.

Figure 5.

How important should the responsibility of different sectors be, and what are the barriers to the assessment and mitigation of environmental risks?

Notes: The figure presents the percentage breakdown of all the respondents’ answers. The left panel presents the distribution of answers to the following question: ‘How important should the contributions and responsibility of the following stakeholders be in the transition to a low-carbon economy? Please rank the following options from the most important to the least important’. The right panel presents the distribution of answers to the following question: ‘Which of the following are barriers for the public and/or private sector in the assessment and mitigation of environmental risks?’.

3.3. Bank risks, asset valuation and portfolio reallocation – does the low-carbon transition matter?

As we mentioned earlier, the financial sector and its behaviour will play a vital role in the impact of climate change on the global economy and the effectiveness of climate mitigation policies. For instance, financing projects that will speed up the transition to a low-carbon economy will make alternatives to fossil fuels more attractive (Mazzucato and Perez, Reference Mazzucato and Perez2014; Krueger et al., Reference Krueger, Sautner and Starks2020). However, financial institutions are also increasingly exposed to both physical and transition risks, which may potentially lead to significant revaluation losses on financial assets. We can therefore expect reallocation from financial investments with high environmental risks to more climate-friendly investments with lower risks. Various events may trigger such reallocation, such as the announcement of new climate policies, increasing public awareness and large unexpected shocks, such as the COVID-19 pandemic or the war in Ukraine.

Carbon risk, or the financial risk associated with carbon emissions, affects carbon-intensive firms due to the anticipated adoption of climate policies. For example, some fossil fuel firms may face devaluation if they cannot utilize their existing fossil fuel reserves (McGlade and Ekins, Reference McGlade and Ekins2015). However, carbon risk extends beyond fossil fuel companies to any firm with a high carbon footprint. A transition risk premium has been observed in equity and options markets, particularly during periods of heightened climate change awareness (Bolton and Kacperczyk, Reference Bolton and Kacperczyk2021; Ilhan et al., Reference Ilhan, Sautner and Vilkov2021, Ramelli et al., Reference Ramelli, Wagner, Zeckhauser and Ziegler2021b). Additionally, Kleimeier and Viehs (Reference Kleimeier and Viehs2016) found that firms voluntarily disclosing carbon dioxide emissions enjoy lower credit costs than their non-disclosing counterparts. Similarly, Alessi et al. (Reference Alessi, Ossola and Panzica2021) show that European individual stock returns exhibit a negative greenium – a risk premium linked to a firm’s environmental performance and transparency.

The existing literature confirms that climate-related policy events, such as the Paris Climate Agreement of 2015 (COP21) and the UN Climate Action Summit in 2019 and related protests, affect the financial sector’s decisions. COP21 increased banks’ awareness of carbon risk (Krueger et al., Reference Krueger, Sautner and Starks2020) and drew their credit away from polluting firms (Reghezza et al., Reference Reghezza, Altunbas, Marques-Ibanez, d’Acri and Spaggiari2022). Furthermore, Delis et al. (Reference Delis, de Greiff and Ongena2018) show that banks have begun pricing the risk of stranded fossil fuel reserves after COP21. The first global climate strike in March 2019Footnote ¹¹ caused a decrease in the stock prices of carbon-intensive firms and led to a downgrade of longer-term earnings forecasts on carbon-intensive firms (Ramelli et al., Reference Ramelli, Ossola and Rancan2021a).

Regarding the COVID-19 pandemic and the war in Ukraine, we still have only limited evidence of their long-term impact on the transition to a low-carbon economy and sustainable investing. Such unexpected shocks can cause governments and investors to lose sight of their environmental consciousness and reevaluate their climate change policy commitment, which inevitably increases (climate policy) uncertainty. Recent studies show that higher political uncertainty is associated with lower equity returns and higher volatility (Brogaard and Detzel, Reference Brogaard and Detzel2015; Brogaard et al., Reference Brogaard, Dai, Ngo and Zhang2020). However, some studies show that sustainable stocks experienced lower volatility (Shields et al., Reference Shields, Ajour El Zein and Vila Brunet2021) and higher resilience (Albuquerque et al.; Reference Albuquerque, Koskinen, Yang and Zhang2020, Engelhardt et al., Reference Engelhardt, Ekkenga and Posch2021) during the COVID-19 period. There are several possible reasons why investors in turbulent times prefer to hold shares of more sustainable firms and limit their exposure to stocks of less sustainable firms. For instance, investors may place higher trust in sustainable firms (Lins et al., Reference Lins, Servaes and Tamayo2017), be more loyal to them (Albuquerque et al., Reference Albuquerque, Koskinen, Yang and Zhang2020; Broadstock et al., Reference Broadstock, Chan, Cheng and Wang2021) or simply have a higher preference for sustainable funds (Riedl and Smeets, Reference Riedl and Smeets2017; Hartzmark and Sussman, Reference Hartzmark and Sussman2019).

In what follows, we reflect on the existing literature to examine the respondents’ views on the relationship between the financial sector and climate mitigation policies. We focus on their expectations regarding banks’ risks, their assessment of green financial asset valuations, and whether financial institutions have altered their asset allocations in response to particular (climate policy) events.

Regarding banks’ risks, we asked how the transition to a low-carbon economy will affect credit, liquidity, market and operational risks. On average, the respondents expect a modest increase in all bank risks, with credit and market risks increasing more than operational and liquidity risks (Figure 6). About 50 per cent of the respondents expect credit and market risks to increase somewhat, while approximately 20–25 per cent anticipate no change. Conversely, 55 and 43 per cent foresee no change in liquidity and operational risks, respectively, and around one-third expect a slight increase in these risks. These views remain consistent across the respondents’ characteristics, except for environmental consciousness and professional roles, where more environmentally conscious respondents (dark blue symbol, right panel) and central bankers (light yellow symbol, right panel) expect a larger increase in banks’ risks due to the transition, with a statistically significant difference for market and liquidity risks.

Figure 6.

How will the transition to a low-carbon economy affect banks’ risks?

Notes: The left panel presents the percentage breakdown of all the respondents’ answers. The right panel quantifies the mean responses of selected groups of respondents. The answers to this question were quantified as follows: significant decrease (−1); some decrease (−0.5); no change (0); some increase (0.5); significant increase (1); and no opinion (NA). We summarize the conversion of verbal answers to numerical ones for all questions in the appendix.

Next, we asked how the respondents would generally describe the market valuation of green financial assets, i.e., assets that investors perceive as environmentally sustainable. According to Figure 7, the respondents, on average, believe that green financial assets (equities, corporate bonds and government bonds) are more or less correctly valued or only slightly overvalued. We found this opinion consistent across the respondents’ characteristics, except for environmental consciousness. Less environmentally conscious respondents are more likely to perceive green financial assets as overvalued, with a statistically significant difference for equities and corporate bonds but not for government bonds.

Figure 7.

How would you generally describe the market valuation of green financial assets?

Notes: The left panel presents the percentage breakdown of all the respondents’ answers. The right panel quantifies the mean responses of selected groups of respondents. The answers to this question were quantified as follows: significantly undervalued (−1); somewhat undervalued (−0.5); neither (0); somewhat overvalued (0.5); significantly overvalued (1); and no opinion (NA). We summarize the conversion of verbal answers to numerical ones for all questions in the appendix.

Last but not least, we asked how financial institutions likely altered their exposure to green and brown firms in response to specific climate policy events (COP21, President Trump’s withdrawal from COP21 and President Biden’s rejoining of COP21), the COVID-19 pandemic and the war in Ukraine. As shown in Figure 8, the respondents’ beliefs generally align with intuition for climate policy events. They assume that exposure to green firms increased following COP21 and President Biden’s announcement, and decreased after President Trump’s withdrawal. Conversely, exposure to brown firms is believed to have decreased after COP21 and President Biden’s announcement, and increased following President Trump’s withdrawal.

Figure 8.

How have financial institutions changed their exposure to green and brown non-financial corporations in response to the following events?

Notes: The left panel presents the percentage breakdown of all the respondents’ answers. The right panel quantifies the mean responses of selected groups of respondents. The answers to this question were quantified as follows: significant decrease (−1); some decrease (−0.5); no change (0); some increase (0.5); significant increase (1); and no opinion (NA). We summarize the conversion of verbal answers to numerical ones for all questions in the appendix.

Interestingly, contrasting expectations exist for the impact of the COVID-19 pandemic and the war in Ukraine. The respondents believe the pandemic had a neutral or slightly positive effect on the transition, increasing exposure to green firms and reducing exposure to brown firms, whereas the war in Ukraine is expected to have a strongly negative impact, with a prevailing belief that exposure to brown firms has increased and/or will increase. The respondents’ opinions significantly differ based on their environmental consciousness and professional roles, with more environmentally conscious respondents (dark blue symbol, right panel) and researchers (orange symbol, right panel) expecting more pronounced portfolio reallocation after climate events.

3.4. How big is the problem of greenwashing?

We usually understand greenwashing to mean a form of marketing used deceptively to convince the public that an organization’s products, goals and policies are environmentally friendly. With the increasing demand for green products and responsible investments in recent years, some companies may have resorted to greenwashing. The literature shows that better ESG performance improves firm reputation (Martínez-Ferrero et al., Reference Martínez-Ferrero, Banerjee and García-Sánchez2016), access to capital (El Ghoul et al., Reference El Ghoul, Guedhami, Kwok and Mishra2011; Cheng et al., Reference Cheng, Ioannou and Serafeim2014) and firm value (Ferrell et al., Reference Ferrell, Hao and Renneboog2016). Although the proportion of companies reporting ESG activities has grown, ESG data in sustainability reports often lack auditing. As for the extent of the problem, for example, Dumitrescu et al. (Reference Dumitrescu, Gil-Bazo and Zhou2022) show that 24 per cent of mutual funds in the US market engage in greenwashing.

To gauge our respondents’ opinions on the prevalence and significance of greenwashing, we asked them to agree or disagree with various statements. The results in Figure 9 reveal that 90 per cent find it difficult to recognize greenwashing, 81 per cent believe undetected greenwashing increases a company’s market valuation and 71 per cent think detected greenwashing should lead to a worse ESG rating. While 60 per cent feel that institutions extensively engage in greenwashing, only 26 per cent support stricter punishment for it compared to other types of fraud.

Figure 9.

Do you agree with the following statements regarding greenwashing?

Notes: The figure presents the percentage breakdown of all the respondents’ answers. % Statement 1: ‘Financial and/or non-financial institutions extensively engage in greenwashing.’ Statement 2: ‘Sometimes it can be difficult to tell whether a certain behaviour is greenwashing or not.’ Statement 3: ‘Greenwashing (if undetected) increases the market valuation of a company involved in this practice.’ Statement 4: ‘Greenwashing (if detected) should lead to a worse ESG rating.’ Statement 5: ‘Greenwashing should be punished more strictly than other types of fraud.’

3.5. How will the COVID-19 pandemic and the war in Ukraine affect the achievement of climate goals?

To put all the other questions into a broader context, we asked the respondents to evaluate the achievability of climate policy goals and the impact of the COVID-19 pandemic and the war in Ukraine on the ability to achieve these goals. The answers should help us see if our respondents perceive the existing climate goals as realistic in light of recent developments. We were interested in the respondents’ opinions on the attainability of three goals: (i) the long-term goal of keeping the increase in the global average temperature to well below two degrees Celsius above pre-industrial levels, (ii) the short-term goal of reducing emissions by more than 50 per cent by 2030 (relative to 1990 levels for the EU and relative to 2005 levels for the US) and (iii) the goal of climate neutrality by 2050 (EU and US goal).

The results in Figure 10 show that respondents are generally more optimistic about achieving the climate neutrality goal than the other two goals. Specifically, about 55 per cent consider it possible to achieve climate neutrality, while about 40 per cent do not. On the other hand, 51 per cent perceive the other two goals as unattainable and about 44 per cent as attainable. All in all, our respondent pool is split roughly half and half on the issue of climate goals. This opinion is relatively consistent across the respondents’ characteristics, with a few exceptions. More environmentally conscious respondents (dark blue symbol, right panel) and central bankers (light yellow symbol, right panel) are more optimistic about the attainability of climate goals, though the difference is not statistically significant.

Figure 10.

Are the following goals achievable? How will the COVID-19 pandemic and the war in Ukraine affect reaching these goals?

Notes: The left panel presents the percentage breakdown of all the respondents’ answers. The right panel quantifies the mean responses of selected groups of respondents. The answers to this question were quantified as follows. Goal achievability: achievable (1); not achievable (−1); and no opinion (NA). How achievability is affected: contributes to reaching goals (1); no effect (0); contributes to missing goals (−1); and no opinion (NA). We summarize the conversion of verbal answers to numerical ones for all questions in the appendix. LT goal: ‘Long-term goal of keeping increase in global average temperature to well below 2°C above pre-industrial levels.’ ST goal: ‘Reducing emissions by more than 50 per cent by 2030 (relative to 1990 levels for EU and relative to 2005 levels for US).’ Neutrality goal: ‘Climate neutrality by 2050 (EU and US goal).’

Regarding the impact of the COVID-19 pandemic and the war in Ukraine, the respondents, on average, expect the war in Ukraine to contribute to missing the climate goals. In contrast, the COVID-19 pandemic is expected to have a limited to no effect. There are notable differences in opinion between respondents from different regions. Respondents in South and Eastern Europe are generally more pessimistic about the impact of the war in Ukraine, with 78 per cent stating that the war will contribute to missing the climate goals. This result is expected, given the high energy demand of industry in these regions. On the other hand, the impact of the COVID-19 pandemic on climate goals is perceived more negatively in North America than in other regions. Surprisingly, respondents from central banks are more optimistic about the impact of both COVID-19 and the war in Ukraine relative to their peers, although they still expect the war to contribute to missing the goals. Environmentally conscious respondents are also, on average, more optimistic, consistent with the pattern seen for other questions.

4.1. Is it all about environmental consciousness?

We start by examining the first two hypotheses, which connect the respondents’ opinions on the economic impact of the transition to their demographic and socioeconomic characteristics. We estimate the impact of these characteristics on the probability of specific answers according to Equation (1).

(1)\begin{equation} BinaryAnswer_{i}^{q} = \alpha^{q} + \beta^{q} EnvinConscious_{i} + \gamma_1^{q} Position_{i} + \gamma_2^{q} Institution_{i} + \delta^{q} X_{i} + \epsilon_{i}^{q}. \end{equation}

The dependent variable $BinaryAnswer_{i}^{q}$ is a binary indicator equal to one if the answer of respondent $i$ to question $q$ is ‘agree’ or positive in some other sense (e.g., ‘increase’, ‘overvalued’ or ‘achievable’). In other words, responses that were assigned positive values in our calculation of the quantified mean response are now assigned a value of one. The variable $EnvinConscious_{i}$ is our measure of environmental consciousness, a characteristic that emerged as a statistically significant determinant in most questions. The variables $Position_{i}$ and $Institution_{i}$ are dummy-coded variables for the respondent’s primary position (researcher vs. non-researcher) and institution (university, central bank, other). The vector $X_{i}$ contains all the remaining dummy-coded demographic variables. We estimate the model only for selected questions where we have identified significant heterogeneity. Coefficient $\beta^{q}$ allows us to verify Hypothesis 1, while coefficients $\gamma_1^{q}$ and $\gamma_2^{q}$ help us to evaluate Hypothesis 2.

In the previous sections, we divided our respondents into two groups based on their self-reported personal contribution to reducing the carbon footprint. For this purpose, we asked them to express whether they agreed or disagreed with 12 statements describing different ways of reducing the carbon footprint (e.g., through their consumption behaviour, financial decisions and environmental activism; see panel (a) of Figure 11). For a detailed list of these statements, see the Appendix. We defined a respondent as environmentally conscious if the number of her ‘agree’ answers was higher than the number of ‘disagree’ answers. We now take a different approach when constructing the variable $EnvinConscious_{i}$. The previous analysis shows that the simple division into two groups significantly affects the respondents’ answers. We therefore now focus more on the intensity of this relationship and define the variable $EnvinConscious_{i}$ not as a binary indicator but as the number of ‘agree’ responses minus the number of ‘disagree’ responses. We then show the distribution of such answers in panel (b) of Figure 11.

Figure 11.

Respondents’ contribution to reducing the carbon footprint. (a) Number of agreeable and disagreeable answers to 12 statements about carbon footprint reduction. (b) The difference between the number of agreeable and disagreeable answers.

Notes: The figure presents the distribution of answers to the question ‘Do you agree/disagree with the following statements about your contribution to reducing the carbon footprint?’. Respondents were asked to rate 12 statements about their contribution to reducing the carbon footprint (e.g., through their consumption behaviour, financial decisions and environmental activism) and to answer whether they agreed or disagreed with these statements.

The results of the linear probability model are presented in Figure 12. Each panel visually presents the estimated coefficient $\beta^{q}$ on the relationship between the respondents’ environmental consciousness and the quantified answer to question $q$ together with 90 per cent confidence intervals. The full regression results are given in Appendix F.

Firstly, the full regression results confirm that environmental consciousness is the most crucial determinant of the differences in the respondents’ opinions, with $EnvinConscious_{i}$ usually being the most significant determinant across all questions (Hypothesis 1). In Section 3.1, we find that environmentally conscious respondents are generally more optimistic, as they are more likely to view climate protection policies as an opportunity than a risk. In panel (a) of Figure 12, we show that this remains true across multiple industries even if we account for the ‘intensity’ of this consciousness. Specifically, one more ‘agree’ answer to one of the 12 statements about reducing one’s personal carbon footprint translates into about a 1–2 per cent higher probability of seeing the transition as an opportunity. Regarding responsibility for the transition, more environmentally conscious respondents are more likely to rank the contribution of households lower than other respondents (panel (b)). The ranking of other sectors, however, remains very similar across the majority of respondents, regardless of their characteristics.

Figure 12.

Linear probability model – the effect of environmental consciousness. (a) Opportunity vs. risk, (b) responsibility for the transition, (c) bank risks and asset valuation, (d) portfolio (re-)balancing, (e) greenwashing and (f) climate goals.

Notes: The figures present the regression results of the linear probability model in Equation (1). Each sub-chart visually presents the estimated coefficient βq on the relationship between the respondents’ environmental consciousness and the quantified answer to question q together with 90 per cent confidence intervals. The full regression results are given in Appendix F. The statements in panel (e) read as follows. Statement 1: ‘Financial and/or non-financial institutions extensively engage in greenwashing.’ Statement 2: ‘Sometimes it can be difficult to tell whether a certain behaviour is greenwashing or not.’ Statement 3: ‘Greenwashing (if undetected) increases the market valuation of a company involved in this practice.’ Statement 4: ‘Greenwashing (if detected) should lead to a worse ESG rating.’ Statement 5: ‘Greenwashing should be punished more strictly than other types of fraud.’

Furthermore, more environmentally conscious respondents are more likely to expect an increase in bank risks due to the transition to a low-carbon economy (panel (c)) and a stronger reallocation of financial assets between green and brown industries in response to specific (climate policy) events (panel (d)). The opinion about the market valuation of green financial assets is not significantly affected by the respondents’ environmental consciousness (panel (c)). Regarding the statements about greenwashing, environmentally conscious respondents are more inclined to impose stricter penalties for greenwashing and are also more likely to concur that detected greenwashing should result in a lower ESG rating (panel (e)). Last but not least, environmentally conscious respondents are more likely to see climate goals as achievable (panel (f)).

Secondly, the full regression results stored in Appendix F confirm the important role of the respondents’ profession in some questions, even though it turns out to be significantly much less frequent than environmental consciousness (Hypothesis 2). Similarly to what we found in Section 3, when we only looked at the respondents’ characteristics individually, central bankers are more likely to identify the transition as a risk than an opportunity (Table A20 in Appendix F) and expect an increase in bank risks in response to the transition (Table 21 in Appendix F). They are also much less likely to punish greenwashing more strictly than other types of fraud (Table A23 in Appendix F).

To address potential concerns about selection bias – particularly given the central role of environmental consciousness in shaping our results – we perform two complementary robustness checks, inspired by the approach of Stroebel and Wurgler (Reference Stroebel and Wurgler2021). Firstly, we construct an alternative index of environmental consciousness and, secondly, we split the sample into two groups based on the original index. The details on robustness checks and their results are provided in Appendix G.

4.2. Climate optimism vs. financial stability risk concerns

In the next step, we extend the right-hand side of Equation (1) by including answers to additional questions from the survey so that we can study the relationship between pairs of those questions. This allows us to identify patterns in respondents’ opinions that might be consistent with Hypotheses 3 and 4. The extended specification is in Equation (2).

(2)\begin{equation} BinaryAnswer_{i}^{q_m} = \alpha^{q} + \beta^{q} QuantifiedAnswer_{i}^{q_l} + \gamma^{q} X_{i} + \epsilon_{i}^{q}. \end{equation}

The dependent variable $BinaryAnswer_{i}^{q_m}$ remains a binary indicator equal to one if the answer of respondent $i$ to question $q_m$ is ‘agree’ or positive in some other sense (e.g., ‘increase’, ‘overvalued’ or ‘achievable’). The new variable $QuantifiedAnswer_{i}^{q_l}$ is the answer of respondent $i$ to question $q_l$ quantified on a discrete scale between $-$1 and 1 according to Table A3 in Appendix C, with ‘agree’ or positive answers (e.g., ‘increase’, ‘overvalued’ or ‘achievable’) being assigned positive values. The vector $X_{i}$ contains all the dummy-coded demographic variables, including the respondent’s environmental consciousness and professional role. Similarly to Equation (2), we estimate the model only for selected pairs of questions where we have identified significant cross-dependencies based on the contingency coefficient.

The results are shown in Figures A2 and A3 in Appendix F. Each panel visually presents the estimated coefficients $\beta_{q}$ of Equation (2) together with 90 per cent confidence intervals. Figure A2 shows the links between questions more in support of the climate optimism hypothesis (Hypothesis 3), while Figure A3 presents results more indicative of the opinions reflecting the financial stability risks associated with the transition to a low-carbon economy (Hypothesis 4).

Regarding climate optimism, we found a positive link between the perceived attainability of climate objectives and viewing the transition as an opportunity. Specifically, individuals who believe climate goals are achievable tend to see the transition as an opportunity rather than a risk, with the likelihood reaching 10 per cent (as seen in panel (a) of Figure A2). This is particularly evident in the energy supply, construction and real estate, transportation and financial sectors.

Similar insights emerge from the relationship between portfolio reallocation following some climate policy events. For example, respondents who believe that the COVID-19 pandemic has contributed to achieving climate goals are more likely to consider an increase in green exposures and a reduction in brown exposures after both COVID-19 and the war in Ukraine (panel (c) of Figure A2). Likewise, those who think the war in Ukraine will help reach climate objectives are more likely to anticipate a rise in green exposures and a fall in brown exposures after the conflict. However, in absolute terms, only a few respondents believe the war will positively impact the attainability of climate goals.

Additionally, those who assume a decline in green exposures after Trump’s withdrawal from COP21 are more likely to view the transition as an opportunity across industries. This suggests that these individuals are more climate optimistic (panel (b) of Figure A2). Conversely, those anticipating a decrease in brown exposures following COP21 and Biden’s rejoining of COP21 are more inclined to see the transition as a risk than as an opportunity. This implies that these respondents are rather pessimistic about the low-carbon transition.

Regarding financial stability risk concerns, we observe a strong connection between participants’ views on changes in banking risks, the attainability of climate goals and their evaluation of the transition as a risk or opportunity across various sectors. Specifically, participants anticipating an increase in banking risks are more inclined to see the transition as a risk than as an opportunity in most sectors, with the likelihood reaching 20 per cent (panel (a) of Figure A3). Moreover, those who regard the neutrality climate goal as achievable are more likely to expect heightened banking risks (panel (b) of Figure A3).

However, respondents who believe the COVID-19 pandemic has contributed to achieving climate goals are more likely to expect a decline in banking risks, which supports the climate optimism hypothesis. This is consistent with the evaluation of the COVID-19 pandemic’s effect on portfolio reallocation, as discussed above. Another indication of perceived climate pessimism is the link to opinions on greenwashing. Namely, respondents who view greenwashing as a widespread issue (64 per cent of respondents) and difficult to detect (90 per cent of respondents) are also more likely to consider the transition to a low-carbon economy to be a risk than an opportunity (panel (c) of Figure A3). This suggests a more pessimistic evaluation of the transition and its economic and financial impacts. In contrast, participants who would penalize greenwashing more severely than other types of fraud (26 per cent of respondents) view the transition more likely as an opportunity.

In summary, we identified patterns in the respondents’ perspectives that extend beyond their individual characteristics and can be ascribed to either an optimistic stance on the transition’s impact (Hypothesis 3) or a more careful viewpoint with indications of financial stability risk concerns (Hypothesis 4). This differentiation does not create a definitive separation dividing all the respondents into two uniform groups. Instead, it highlights possible factors and connections in evaluating the various anticipated economic and financial consequences of the transition to a low-carbon economy, which may be shaped not only by differences in technical proficiency or exposure to climate-related research but also by higher-order beliefs – that is, beliefs about how others perceive climate risks – as suggested by Mildenberger and Tingley (Reference Mildenberger and Tingley2019) and Bauer et al. (Reference Bauer, Goedker, Smeets and Zimmermann2024).