Green Finance – Bottom-Up Tracking: Comprehensive Study Notes

Page 1

Document title: “Green Finance: A Bottom-Up Approach to Track Existing Flows”
Produced by: IFC – Climate Business Department, Climate Finance & Policy Team (2016)
Position within World Bank Group implied (IFC member).
Purpose signalled: Track existing green-finance flows using a bottom-up lens.

Page 2 – Acknowledgements

Draft for consultation.
Core partners:
- IFC Climate Business Department (Director: Christian Grossmann).
- Climate Finance & Policy Team (Head: Vikram Widge).
- GIZ (German Gesellschaft für Internationale Zusammenarbeit).
Lead authors: Laura Bergedieck, Aditi Maheshwari, Francisco Avendaño Ugaz.
Peer reviewers (Washington DC IFC): Ahmed Aliyu Ahmad, Liane Asta Lohde, Samuel Maimbo, Marc Schrijver, Peer Stein, Wei Yuan, Nina Zegger.
Layout: Esther Rojas-Garcia.
Legal/rights caveats: IFC owns copyright; reproduction allowed for education/non-commercial; IFC disclaims responsibility for errors, maps do not imply territorial judgment.
IFC is an international organisation; “International Finance Corporation” and “IFC” are registered trademarks.

Page 3 – Contents snapshot

Executive Summary p.3
Section 1: Context & Objectives (Background; Objective; Definitions).
Section 2: Tracking Green Finance in Banking (Methodology; Demand; Supply; Sub-steps Define – Estimate – Aggregate – Results).
Section 3: Bond Market & Institutional Investors (Green bonds; Institutional investors).
Section 4: Conclusions & Recommendations (Short-term, Medium-term).
Annex: Country table of syndicated green-loan amounts.

Page 4 – Executive Summary (1)

Transition to net-zero & sustainable economy ⇒ need to scale up green finance.
Definition: Green finance = financing of investments that provide environmental benefits.
Comparing private-sector supply vs. country demand ⇒ pin-point action to close gaps.
Current landscape: Many institutions have their own green definitions; no harmonised system, so progress hard to measure.
Main contribution: IFC team builds bottom-up banking-focused tracking framework:
1. Define green at project level via intended use.
2. Apply share-of-project that is green.
3. Aggregate industry & country totals.
4. Compare to need.
Key implementation challenges: inconsistent typology, missing data, sector-class mismatch, poor Use-of-Proceeds (UoP) tagging.

Page 5 – Executive Summary (2) – Detailed Challenges

Defining green / suitable estimates
• Project level: UoP rarely filled or mis-tagged (e.g.
“Project Finance” vs. “Clean Energy”).
• Sector level: Can use industry share estimates (e.g. certified buildings) but sector codes differ across datasets.
• Company level: Green-revenue % exists only for limited listed firms.
Aggregating data
• By borrower location: often HQ ≠ project site.
• By financier: loan participations & lender domicile often missing.
• Combining datasets: no universal identifiers, multiple coding systems.
Comparing supply & demand
• Supply remains rough estimate.
• Demand metrics need translation of national policy (Paris, SDGs) into sector-level finance needs per instrument.

Page 6 – Exec Summary (3) – Early Results & Two Other Segments

Banking (syndicated-loan test case, Thomson Reuters 2014)

DEFINE: Used borrower industry.
ESTIMATE: Applied green % proxies:
- $100\%$ Clean Energy.
- $0\%$ Oil & Gas / Petrochem / Pipelines / Coal Power.
- $17\%$ Real Estate (certified green buildings).
- $13\%$ Food-Beverage / Paper-Forest / Agriculture.
- $10\%$ Infrastructure & Transport.
- $0.1\%$ Automobiles (EV share).
AGGREGATE: by industry & borrower country.
RESULTS:
- 82 % of 2014 syndicated-loan count in sectors with some green activity.
- 15 % of 2014 loan $amount (=$ \approx 164.7\text{ bn} $) classified green.</li><li>Within partly-green set:$ 38 \% $green real-estate,$ 31 \% $clean-energy.</li><li>Country shares (of green$ ): US 35 %, UK 8 %, China & India 4 % each.

Bonds

Green Bond Principles (GBP) give consistent global tracking.
Global bond market $\approx 90\text{ trn}$ ; climate-aligned bonds $694\text{ bn}$ ; of those labelled green $118\text{ bn}\,(17\%)$ .

Institutional investors

Many initiatives (e.g. PRI), but few integrate ESG deeply into core decisions.
$1{,}072$ investors report to PRI; only a minority use ESG in fundamentals.

Page 7 – Executive Summary (4) – Early Recommendations Table

Stakeholder matrix listing short- (S) & medium-term (M) actions:

Multilateral Orgs: analyse demand (S), pilot supply-demand studies (M).
National Regulators: articulate national needs (S), craft typologies & rules (M).
Private sector: improve UoP tagging (S), integrate green-revenue data (M).
Data / Standard setters: harmonise IDs & industry codes (S), embed green data in corp-reporting & offer new services (M).

Page 8 – Introduction (context)

Finance has high leverage over real economy; transition needs two pillars:
1. Transparency on flows with environmental benefit.
2. Metrics to empower financiers backing such flows.
Global momentum: G20, FSB, Paris Agreement (Article $2.1(c)$ ).
Present < $1\%$ of bonds and institutional holdings are tagged green.
No systematic global approach; China rough estimate: $10\%$ of banking assets green.
G20 GFSG (2016) identified seven recommendations; this report tackles #7 (measurement).

Page 9 – Section 1: Background & rationale

G20 GFSG mandate: Identify barriers & options to scale private green finance.
Published synthesis focused on: Banking, Bonds, Institutional Investors; plus Risk & Metrics.
Key GFSG recommendations summarised (strategic signals, principles, networks, local markets, cross-border, risk forums, better definitions).
Inadequate data & standards → information asymmetry & capital misallocation.

Page 10 – Objectives & Definition Landscape

Objective here: Approximate private green-finance flows via bottom-up.
Greening finance means both recognising risks & catalysing positive impact.
Evidence suggests $\uparrow$ sustainability ≈ $\uparrow$ or neutral financial performance.
Survey showed common sectoral coverage but fragmented tracking.
List of typical “green” sectors: Adaptation, CCS, EE, Env. Protection, Green Buildings, Products, Renewables, Sustainable Land, Transport, Waste, Water.
Table of actors & definitions (stock exchanges, SBN, PRI, PSI, CDP, SASB, IIRC, CDSB, IFC PS, Equator, CBRC, Bangladesh, France Art.
173, EU NFR, UNFCCC GCF, OECD Centre, IDFC, MDBs).

Page 11 – Further Definitions & Emerging Tracking Initiatives

Observation: Most definitions focus on eligibility; few track flows/impact.
China sole example of mandatory green-loan disclosure by banks (12 categories, ~$~ $\approx10\%$ share).
Bottom-Up industry initiatives in Table: FTSE LCE green-revenue model (>13k companies), FSB TCFD (climate risk disclosure), Portfolio Carbon Initiative, PDC, Climpax.
Hybrid attempts (bottom-up + policy alignment): SEI Metrics, 2° Investing Initiative (2DII) Climate Capital Monitor.

Page 12 – Section 2 Intro & Methodology (overview)

Four-step process for banks: Define → Estimate → Aggregate → Compare (Figure 1).
2DII schematic: Data vs. assets vs. policy challenge slide (Figure 2).

Page 13 – Demand-Side Concept

Need proxy for “sufficient” green finance (demand), ideally disaggregated by instrument.
Requires translating NDCs/SDG into sectoral finance targets.
Few countries have explicit private-finance mobilisation targets.

Page 14 – Supply-Side: Data Universe for Loans

Focus on syndicated loans (largest share of bank business).
Data providers: BIS, Bloomberg, Bureau van Dijk, IFC, IMF, Thomson Reuters.
Three granularity layers (Figure 3): Project → Company → Country/Bank.
Thomson Reuters 2014 syndicated-loan dataset: $4{,}412$ loans, $1.1\text{ trn}$ face.

Page 15 – Define Step in Depth

UoP category list (127 sub-tags, only 24 used).
IFC survey green-sector list adopted as inclusion filter.
Table of weaknesses: miscoding of UoP; need sub-field “green” tag.

Page 16 – Estimate Step – Sector & Company Proxies

Three estimation tiers (Figure 4):
1. Project-level (exact UoP) ⇒ direct $100\%$ green.
2. Sector-level (apply %).
3. Company-level (green revenue share).
Sector codes vary (GICS, ICB, ISIC, NAICS, NACE, ANZSIC); Thomson uses combined TF Macro/Mid.

Page 17 – Chosen Proxy Values

Clean Energy TF-Mid codes & “Hydro/Wind” words ⇒ $100\%$ .
Fossil/Coal ⇒ $0\%$ .
Real Estate ⇒ $17\%$ (avg of 24 % Green Building survey & 9.7 % US Energy-Star homes).
Automobile ⇒ $0.1\%$ (EV sales share).
Food/Ag/Pulp ⇒ $13\%$ (avg certifications).
Infrastructure & Transport ⇒ $10\%$ (best-guess; few data sources yet).

Page 18 – Aggregate Step & Identifier Issues

Aggregation options: by borrower HQ (chosen) or by lender but lender-share data missing.
Cross-dataset linking hindered by multiple IDs (Ticker, ISIN, CUSIP, SEDOL).

Page 19 – Results (Global Totals)

Green $share:$ \approx 164.7\text{ bn} / 1.1\text{ trn} = 14.95\% $.</li><li>Loan count basis:$ 3{,}610 / 4{,}412 = 82\% $loans touch partly-green sectors.</li></ul><h4 id="75e67938-87c6-4102-9e24-ddead6569605" data-toc-id="75e67938-87c6-4102-9e24-ddead6569605" collapsed="false" seolevelmigrated="true">Page 20 – Results by Sector</h4><ul><li>Within green$ universe:
- Real Estate $\approx38 \%$ .
- Clean Energy $31 \%$ .
- Infrastructure/Transport $\approx 17 \%$ .
- Food/Ag/Paper $\approx 8 \%$ .
- Automobiles negligible.

Page 21 – Results by Country (Top & Emerging)

Shares of global green-loan $:<ul><li>US$ 35\% $, UK$ 8\% $, AU$ 6\% $, FR$ 6\% $, JP$ 5\% $.</li><li>EM leaders: China$ 4.2\% $, India$ 4.0\% $, Turkey$ 2.5\% $, UAE$ 0.8\%.

Among WB-client EMs: China & India > 6\text{ bn} each; Turkey > 4\text{ bn} $; Ghana/Chile/Indonesia/Mexico/Brazil$ >0.6\text{ bn} $.</li><li>Outlier: Turkey green-loan share$ 72\% $of its total loans (dataset bias).</li></ul><h4 id="2ed2e2e2-8270-4f08-bef0-6fd498762b95" data-toc-id="2ed2e2e2-8270-4f08-bef0-6fd498762b95" collapsed="false" seolevelmigrated="true">Page 22 – Plausibility Checks</h4><ul><li>China CBRC data:$ 10\% $green-loan share (vs. our 12 % for CN).</li><li>IFC client survey: avg$ 6\% $climate lending share ($ \approx 4.5\text{ bn}).

Differences explained by: data bias (US-centric), coarse proxies, limited EM coverage.

Page 23 – Section 3: Green Bonds – Definitions

GBP (ICMA, 2014): 4 pillars (UoP, evaluation, proceeds mgmt, reporting).
Climate Bond Standard (CBI) builds on GBP, sector taxonomy, 3rd-party review.
National regimes:
- China: PBoC Green Bond Guidelines + Project Catalogue (mandatory UoP & quarterly reporting).
- India: SEBI rules (GBP-aligned; UoP disclosure in annual report).
- France: TEEC fund label requires GBP-compliant allocation.
- Stock exchanges (London, Luxembourg, Shanghai, Shenzhen, etc.) creating green-list segments.

Page 24 – Green-Bond Market Data

CBI/HSBC 2016 snapshot (July):
- Global bond mkt 90\text{ trn} $.</li><li>Climate-aligned bonds$ 694\text{ bn} $; of which labelled$ 118\text{ bn} $(≈$ 17\% $).</li><li>Thematic split (all climate aligned): Transport 33 %, Energy etc.</li><li>Labelled subset dominated by Buildings & Energy (58 %).</li><li>60 % of labelled GBs get external reviews.</li></ul></li><li>2015 issuance record$ 47.8\text{ bn} $; 2016 forecast$ 80\text{ bn} $.</li><li>2016 H1 Chinese issuance$ 18.5\text{ bn} $(42 % of world).</li><li>Still only$ 0.15\% of global fixed-income.
Page 25 – Section 3: Institutional Investors
- 19 investor climate initiatives grouped: Measure / Engage / Reallocate / Reinforce.
- Critical initiatives:
 - Portfolio Decarbonization Coalition & Montreal Pledge (carbon footprint disclosure).
 - CDSB fiduciary duty statement.
- PRI membership (Sept 2016): 1{,}553 $signatories,$ 62\text{ trn} $AUM.</li><li>2015 reporting:$ 455 $signatories hold$ 1.3\text{ trn} $ESG-themed AUM ⇒$ 2.1\% $of signatory assets.</li><li>Within listed equity holdings:<ul><li>$ 95\% $claim some ESG incorporation; only$ 30\% $do fundamental integration;$ 16\% track influence systematically.
Legal reviews in 7 G20 countries: ignoring material green factors may breach fiduciary duty.

Page 26 – Section 4: Conclusions

Possible to approximate green flows but accuracy limited by data gaps.
Bonds segment offers template (GBP).
Banking needs better UoP & sector granularity; investors need concrete criteria.
Full 360° picture demands project-level tagging of ‘green’.

Page 27 – Recommendations (Short-Term)

Multilaterals: map client demand; convene on typology.
Regulators: study current practice; derive policy indicators; push data transparency.
Private banks: clean up UoP coding.
Investors: embed ESG in routine analysis.
Data/Standards: raise awareness; harmonise IDs & sectors.

Page 28 – Recommendations (Medium-Term)

Multilaterals: pilot supply-vs-demand studies; implement harmonised standards; align bottom-up with macro models.
Regulators: craft green rules for loans, bonds, portfolios; learn from China.
Banks & Investors: replicate GBP success for loans/equity; use green-revenue metrics (e.g. FTSE LCE).
Data/Standards: push green-revenue disclosure via CDP/GRI/IIRC; create new commercial datasets & services.

Page 29 – Annex (Country Table Highlights)

Global green-loan total 2014: $164.7\text{ bn}$ .
Top 5 borrower HQs:
1. US $56.8\text{ bn}$ (34.5 %).
2. UK $13.0\text{ bn}$ (7.9 %).
3. Australia $10.2\text{ bn}$ (6.2 %).
4. France $9.2\text{ bn}$ (5.6 %).
5. Japan $8.3\text{ bn}$ (5.1 %).
Emerging-market leaders: China, India, Turkey, UAE.
Long tail: 80+ countries with < $1\text{ bn}$ each.

End of detailed page-by-page study notes.