Can Prudential Regulation Help the Transition to a Green Economy?

By | February 22, 2019

Courtesy of Lorenzo Esposito[1], Giuseppe Mastromatteo[2] and Andrea Molocchi[3]

Among the many lessons learned by the financial industry, central banks, and economists in the wake of the global financial crisis, the most important may be the relevance of systemic risk. In a market dominated by multinational financial conglomerates, developments and disruptions in one country can quickly spread to others. The effects of climate change are felt in all countries, and in this sense, it is the ultimate systemic risk. Policymakers are beginning to ask how the financial system can not only withstand this systemic risk, but help usher in a new, low-carbon economy (G20, 2016).

There are various routes through which environmental risks influence banks’ financial risks. Some are more direct, such as the risks connected to catastrophic events with the ensuing legal consequences, where firms are held liable for environmental damages that affect their creditors. Others are rather indirect, including the so-called transition risk linked to the expected cost of forthcoming environmental policies (Batten et al., 2016).

The environmental policy framework of financial risks was transformed by the Paris Agreement on climate change, which required signatory countries to make “finance flows consistent with a pathway towards low greenhouse gas emissions and climate-resilient development.” In addition, financial firms are beginning to incorporate climate change into their business models, and financial regulators in certain jurisdictions are thinking of how they can facilitate a more sustainable economy (EU, 2018).

A prudential regulation proposal: “Environment-risk weighted assets”

In a recent paper[4] we propose modifying the calculation of Basel III risk weighted assets to internalize the pollution risk of the borrower. In practice, a bank’s asset would first be multiplied using the present prudential regulation risk-weight and then multiplied by a pollution coefficient that represents a correction for the environmental risk, thus determining an environment risk-weighted asset (ERWA):





ai is the book value of the considered asset (i is the sector in which the considered asset is into);

ri is the weight assigned to the considered asset according to the present framework for banking regulation (see BCBS, 2017, table 1);

ci is the pollution coefficient representing the environmental impact associated to the considered asset;

ei is the final regulatory measure of the considered asset, weighted both for financial and environmental risk associated to the asset.

This modification of RWAs will better capture all the risks embedded in a bank’s assets and facilitate the funding of “green” projects through the establishment of internationally agreed upon capital standards; a far more effective method compared to trying to get multiple counties to agree upon common fiscal standards to promote green development.

Our proposal also includes an operation framework. For instance, in terms of the value of c, we suggest a range of 1.5-0.5, where the minimum weight is only assigned to economic activities with zero, or positive, environmental externalities. We also suggest gradual implementation of ERWAs, in accordance with standard Basel Committee practice.

Building and applying ERWAs

There are many ways to build ERWAs; here, we present two examples. A simpler approach is to first obtain pollution coefficients ci, based on CO2 emissions. These emissions are related to the added value of the asset’s economic activity, given that CO2 is the most important greenhouse gas and its emissions have been extensively analyzed in literature and by institutions (for instance within the UN Framework Convention on Climate Change). In practice, a sectoral coefficient (but in a more advanced stage, a coefficient for every credit line) can be calculated as a deviation from the national average. A more complete measurement can be based on the external costs of the activity that takes into account both environmental impact factor data (e.g., emissions due to the activity of the borrower) and the associated monetary values of the main risks related to these impact factors.

A more complex approach is based on considering the whole production chain – from the extraction of raw materials to final consumption – as “activity,” thus taking into account direct and indirect pollution embedded in every asset. This measurement can be made using Input-Output Analysis (IOA) that has been largely used in product environmental assessment (Serrano, 2007, and Igos et al., 2015), which allows for the consideration of both the direct and indirect pollution associated with a commodity, something that is increasingly important due to the growing complexities of global value chains.

A first tentative application to Italian data

We have begun a first analysis of available sectoral data regarding the Italian economy. For the direct external cost approach, we have used sectoral external costs per value added (EEC/VA column in table 1 and 2) taken from Molocchi (2017). To simplify, we excluded public administration, financial services and artistic activities because they contribute 27% of Italian Value Added but only 1.7% of external costs. To the remaining 73% of Value Added, credit extended by banks was €871.3 billion (fifth column). We calculated the sectoral coefficient based upon how much the sector’s external costs deviate from the benchmark (the national average) and use the results to assign a “green” or “brown” label in the fourth column of Table 1. We applied a smoothing factor to keep the pollution coefficient of non-service sectors in the 0.5–1.5 range.

Table 1: Non-manufacturing Sectors ERWAs, Italy 2013

Click the table to enlarge

Source: Authors’ elaboration, based on Molocchi, 2017. Credit data source: Bank of Italy Centrale dei rischi.

To understand Table 1, take the first line (agriculture, forestry and fishing); the sector contributes 2.3% of the total value added to the Italian economy, its total external costs (air pollution, greenhouse gases, noise, etc.) are worth 32.4% of its value added, and it absorbs 5.1% of the total credit supplied to Italian firms. Therefore, Table 1 tells us that €100 lent to an agricultural firm requires the bank to hold €113 in capital (ERWAs).

Results for manufacturing sectors are as follows:

Table 2: Manufacturing Sectors ERWAs, Italy

Click the table to enlarge

Source: Authors’ elaboration, based on Molocchi, 2017. Credit data source: Bank of Italy Centrale dei rischi.

Under the external costs approach, manufacturing activities obtain a pollution coefficient ranging from 0.56 to 1.04. The coefficients are higher than the average in 5 sectors. It’s interesting to note that, together, green sectors account for 74% of the total credit. This means that even now, applying ERWAs would not imply an overall increase in total capital requirements. Higher capital requirements would be concentrated in a few sectors, while most activities correspond to lower requirements.

We also applied the third suggested approach – looking at the entire production chain – to calculate pollution coefficients based upon the CO2 intensities embodied in final demand sectors calculated by Mongelli et al. (2009) (unfortunately, this data is from a different year (2000). For comparisons sake, we also included the direct external cost-based pollution coefficients (2013) and the embodied CO2 emission coefficients (2000) in Tables 3 and 4 below.

Table 3: Non-manufacturing Sectors ERWAs and IOA ERWAs, Italy 2013 and 2000

Click the table to enlarge

Source: Authors’ elaboration, based on Molocchi (2017) and Mongelli et al. (2009)

In just one out of nine comparable cases, an economic sector is considered differently depending on the method applied, either because the external costs assessment is not limited to CO2 emissions and/or because IOA assumes a different distribution of emissions responsibilities along the supply chain. Results for manufacturing sectors are the following:

Table 4: Manufacturing Sectors ERWAs and IOA ERWAs, Italy 2013 and 2000

Click the table to enlarge

Source: Authors’ elaboration, based on Molocchi (2017) and Mongelli et al. (2009)

For manufacturing sectors, in 3 cases (out of 11), a subsector is green under the direct method and brown using IOA, for one sector the opposite is true. Together, these 4 subsectors account for 11% of total credit to Italian firms.


 There is intense discussion underway around how the financial system can be harnessed to address climate change, but apart from the issuance of green bonds, real solutions have been hard to come by. We need practical tools that can be rapidly deployed to align the lending policies of banks with a more sustainable economy. The necessity of these tools is highlighted by the fact that the estimated annual environmental external costs from global human activities are 11% of world GDP (UNEP, 2011).

We believe ERWAs are an effective tool because they can be applied to every bank asset and can account for every source of pollution. Our suggestion is to start applying the direct sectoral emission intensities approach to corporate lending (and corporate bonds) and the IOA method to households lending. This approach can be gradually extended to include, besides CO2 emissions, all the other main impact factors that can be conventionally calculated through the external costs approach. Our preliminary results of applying ERWAs in a national context (Italy) show that the tool is sufficiently stable to be used in the green transition without provoking a shock within the banking system.



Batten, Sandra, Rhiannon Sowerbutts, and Misa Tanaka. 2016, “Let’s talk about the weather: the impact of climate change on central banks.” Bank of England Staff Working Paper No. 603,

BCBS. 2017. “High-level summary of Basel III reforms.”

  1. 2018. (High Level Expert Group on Sustainable Finance). “Final Report.” January,

G20. 2016. G20 Green Finance Synthesis Report. Green Finance Study Group,

Igos, Elorri, Benedetto Rugani, Sameer rege, Enrico benetto, Laurent Drouet, Daniel S. Zachary. 2015. “Combination of equilibrium models and hybrid life cycle–input-output analysis to predict the environmental impacts of energy policy scenarios.” Applied Energy 145: 234-245.

Molocchi, Andrea. 2017. “Polluters Make Others Pay.” Nuova Energia, No. 1 and 2,

Mongelli, Ignazio, Giuseppe Tassielli and Bruno Notarnicola. 2009. Carbon Tax and its Short-term Effects in Italy: An Evaluation Through the Input-Output Model, in S. Suh (ed.), Handbook of Input-Output Economics in Industrial Ecology, Berlin: Springer.

Serrano, Monica. 2007. “The Production and Consumption Accounting Principles as a Guideline for Designing Environmental Tax Policy.”, FEEM Nota di Lavoro 8.

UNEP. 2011. Universal Ownership. Why environmental externalities matter to institutional investors, Geneva,


[1] Banca d’Italia, Milan Branch and Economic Policy Department, Università Cattolica del Sacro Cuore, Milan.

[2] Economic Policy Department, Università Cattolica del Sacro Cuore, Milan.

[3] Ministero dell’Ambiente e della Tutela del Territorio e del Mare – Unità Assistenza Tecnica Sogesid S.p.A.

[4] The views expressed in the paper and in this work are those of the authors and do not involve the responsibility of the Bank of Italy, of the Ministry of Environment or Sogesid SpA.

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