Where and how machine learning plays a role in climate finance research

Figures & data

Table 1. SPAR-4-SLR protocol. Assembling, arranging and assessing.

Assembling
Search Keywords:
ALL = (‘climate change’ OR ‘ESG’ OR ‘sustainable finance’ OR ‘green finance’ OR ‘climate finance’) AND AB = (finance OR ‘financial market*’ OR bond* OR investment*OR corporate* OR funding OR financing) AND ALL = (‘lasso’ OR ‘random forest*’ OR ‘extreme gradient’ OR ‘xgboost’ OR CART OR ‘deep learning’ OR ‘neural network’ OR ‘machine learning’)
Search Databases:
1. Web of Science (WoS)
2. Google Scholar (GS)
3. Dimensions.ai (D.AI)
Search Result:
1. Web of Science (WoS): 125 documents
2. Google Scholar (GS): 18,300 documents -- 45 search pages screened, approx. 450 results.
3. Dimensions.ai (D.AI): 127 documents
Arranging
Organizing Filters:
Filtered Year for Inclusion:	1999–2022
Filtered Area for Inclusion:	Environmental Science, Computer Science, Economics Finance
Filtered Document Type for Inclusion:	Article
Filtered Publication Stage for Inclusion:	Final
Filtered Source Type for Inclusion:	Journal Article, Working Paper, Conference Proceedings, Book chapter.
Filtered Language for Inclusion:	English
Find duplicates:	Using Endnote bibliographic manager
Ex-post external validation:	Based on field expertise (Human-in-the-Loop).
Filtered Search Result:	217 documents
Assessing
Analysis Methods:
Performance analysis:	Publication trend, Evolution of model choice by topic, Breakdown of Journal and Publication type
Topic modelling	Latent Dirichlet Allocation (LDA), using Python.
Agenda Proposal Method:	Reading of articles and reflection of text extracts including mention on machine learning models.
Reporting Convention:	Figures, tables and words.
Limitations:	Accuracy of search results, specially in GS.
	Completeness of references in Environmental Science with a focus on finance.
Support:	No funding received

Table 2. Probabilities of tokens, per topic.

LDA Topic		1		2		3		4		5
	Tokens	Probability of token per topic (${\beta }_k$)	Tokens	Probability of token per topic (${\beta }_k$)	Tokens	Probability of token per topic (${\beta }_k$)	Tokens	Probability of token per topic (${\beta }_k$)	Tokens	Probability of token per topic (${\beta }_k$)
	activ	0.026	risk	0.026	sustain	0.018	biodivers	0.012	carbon	0.028
	csr	0.017	flood	0.023	chang	0.011	financi	0.012	soil	0.024
	valu	0.012	algorithm	0.012	studi	0.01	green	0.011	invest	0.016
	flood	0.01	predict	0.011	method	0.009	base	0.011	predict	0.011
	storag	0.01	price	0.01	financ	0.009	develop	0.01	power	0.011
	correl	0.01	term	0.009	polici	0.009	invest	0.01	polici	0.011
	corpor	0.01	differ	0.007	research	0.008	resourc	0.01	emiss	0.01
	signific	0.01	impact	0.007	topic	0.008	cost	0.009	studi	0.01
	avail	0.009	provid	0.007	inform	0.008	conserv	0.008	result	0.009
	base	0.009	studi	0.007	train	0.008	research	0.008	forecast	0.009
Econ. Label		*discarded*		Natural hazards		*discarded*		Biodiversity		*discarded*
LDA Topic		6		7		8		9		10
	Tokens	Probability of token per topic (${\beta }_k$)	Tokens	Probability of token per topic (${\beta }_k$)	Tokens	Probability of token per topic (${\beta }_k$)	Tokens	Probability of token per topic (${\beta }_k$)	Tokens	Probability of token per topic (${\beta }_k$)
	carbon	0.026	chang	0.027	esg	0.07	energi	0.03	compani	0.02
	price	0.023	crop	0.024	invest	0.024	predict	0.019	corpor	0.019
	market	0.02	yield	0.019	rat	0.022	emiss	0.016	report	0.018
	emiss	0.018	futur	0.014	social	0.021	carbon	0.015	financi	0.018
	firm	0.016	agricultur	0.013	portfolio	0.021	forest	0.012	disclosur	0.017
	green	0.015	adapt	0.011	compani	0.013	result	0.01	csr	0.016
	financ	0.013	product	0.011	perform	0.013	chang	0.009	perform	0.014
	paper	0.012	hybrid	0.011	stock	0.013	use	0.008	risk	0.013
	stock	0.01	project	0.01	risk	0.012	random	0.008	relat	0.012
	sector	0.01	suitabl	0.01	score	0.012	impact	0.008	environment	0.011
Econ. Label		Carbon markets		Agricultural risk		ESG factors investing		Energy economics		Climate data

Figure 1. Visualization of topic 9 (energy economics).

Table 3. Descriptive statistics of corpus.

	Peer-reviewed	Non-peer reviewed
	Journal	Working Paper	Conf. Proceeding	PhD Dissertation	Book Chapter	Total
Biodiversity	15	6	3	0	0	24
Natural Hazards	19	2	3	1	0	25
Agricultural risk	17	4	3	0	1	25
Physical Risks: Subtotal	51	12	9	1	1	94
Energy economics	44	10	2	1	1	58
Carbon Markets	12	2	1	2	1	18
Transition Risks: Subtotal	56	12	3	3	2	76
ESG factors investing	17	14	2	0	0	33
Climate data	12	13	6	3	0	34
CSR: Subtotal	29	27	8	3	0	67
Total	136	51	20	7	3	217

Figure 2. Number of publication (cumulative), per year and topic.

Figure 3. Total number of publication, by type of publication science.

Figure 4. ML methods used, overall.

Figure A1. Rate of perplexity change and latency of training.

Figure A2. Coherence score and latency of training.

Figure A3. Visualization of topic 6 (carbon markets).

Figure A4. Visualization of topic 8 (ESG factors & investing).

Figure A5. Visualization of topic 10 (climate data).

Figure A6. Visualization of topic 4 (biodiversity).

Figure A7. Visualization of topic 2 (natural hazards).

Figure A8. Visualization of topic 7 (agricultural risk).

Figure A9. ML methods used in physical risks.

Figure A10. ML methods used in transition risks.

Figure A11. ML methods used in CSR.

Table A1. Original root words from stem.

Stem	Original words
activ	activity, active
adapt	adapt, adaptation
agricultur	agriculture, agricultural
avail	availability, available
base	based, baseline
biodivers	biodiversity, biodiverse
chang	change, changing, changer
compani	company, companies
conserv	conservation
corpor	corporate, corporation, corporatism
correl	correlation, correlate, correlated
cost	cost, costly, costing
develop	development, developing
differ	difference, different, differently
disclosur	disclosure, disclosures, disclosed
emiss	emission, emissions, emitting
energi	energy, energies
financ	finance, finances, financial, financially
financi	financial, financially
firm	firm, firms
flood	flooding, floods, flood
futur	future, futures
hybrid	hybrid hybridization
inform	inform, information, informative
invest	investment, investor, investing
method	method, methodology, methodological
polici	policy, policies, policymaker
predict	prediction, predictability, predictable
product	product, production, productive
project	project, projects, projecting
provid	provide, provider, providing
rat	rating, rated
relat	relate, related, relationship
resource	resource, resources, resourceful
risk	risk, risky, riskiness
signific	significance, significant, significantly
stock	stock, stocks
storag	storage, store, stored
studi	study, studied, studying
suitabl	suitable, suitability, suitably
sustain	sustainability, sustainable, sustainably
term	term, terminology
train	training
valu	value, valuation, valuable

Table A2. Type of ML model used.

	Linear regression/Logit	Lasso/Ridge/Elastic Net	Decision Trees	Random Forest	Generalized Boosting Models	Genetic Algorithm	Fuzzy Logic	Artificial Neural Networks	Reinforcement Learning	K-nearest neighbors	K-means	Support Vector Machine	Naïve Bayes	Image classification/ OCR	Natural Language Processing	Ensemble Model	Causal ML	Online ML	Symbolic AI	Interpretable ML (e.g. SHAP)
Physical Risks	5	5	7	24	10	5	2	23	0	5	3	12	0	8	8	3	2	1	0	3
Transition Risks	11	15	8	21	9	1	3	38	2	6	2	7	1	6	5	8	3	0	0	3
Corporate & Social Responsibility	6	8	4	14	6	1	0	13	1	2	1	7	2	0	31	3	1	0	2	3
Total	22	28	19	59	25	7	5	74	3	13	6	26	3	14	44	14	6	1	2	9
% Total	6%	7%	5%	16%	7%	2%	1%	19%	1%	3%	2%	7%	1%	4%	12%	4%	2%	0%	1%	2%

Table A3. Corpus of documents. ML methods, by topic (Physical Risk).

Application domain		List of papers	List of ML models
Physical Risks	Natural Hazards	Bayle et al. (2020), Manandhar et al. (2020), Biffis and Chavez (2017), Chen et al. (2020), Cesarini et al. (2021), Lyubchich et al. (2019), Hoang et al. (2020), Inyang, Akpan, and Akinyokun (2020), Bjånes, De La Fuente, and Mena (2021), Nti et al. (2021), Rohayani et al. (2021), Avand et al. (2021), Shu et al. (2022), Yang et al. (2021), Diniz et al. (2021), Best et al. (2021)	Markov-CA (deep learning), Image classification, Random forest, Genetic algorithms, K-means, ANN, SVM, XGBoost, LSTM (Recurrent Neural Network), Extra trees, Regression model, CART (Decision Trees), Multi-layer Perceptron (deep learning), Adaptive Neuro Fuzzy Inference System, Back-propagation Neural Network, Ensemble model
	Biodiversity	Floreano and de Moraes (2021), Wang et al. (2018), Dao et al. (n.d.), Lima et al. (2022), da Silveira et al. (2021), Keys, Barnes, and Carter (2021), Macadam, Nowell, and Quigley (2021), Pearson et al. (2020), Dao et al. (2019), Santamaria et al. (2020), Reiersen et al. (2021), Rakova and Winter (2020), Hou et al. (2020), Seidl et al. (2020), Evans et al. (2010), Bastien-Olvera and Moore (2021)	Linear Regression, Decision Tree, Naive Bayes, Support Vector Machine, Random forest, Artificial Neural Network (ANN), K-Nearest Neighbours, Boosting Ensemble meta-algorithm, Reinforcement learning, Deep multi-agent reinforcement learning, Kernel Extreme Learning Machine, Stacked denoising autoencoders, Wavelet Neural Network, Genetic Algorithm, Particle Swarm Optimization, Bagging, Causal Direction from Dependency (D2C) algorithm, LightGBM (Gradient boosted decision trees), CatBoost, XGBoost, SHAP, Optical Character Recognition (OCR), Natural Language Processing (NLP), Interpretable trees, K-means, LSTM (Recurrent Neural Network), Double debiased ML, Radial Artificial Neural Network, Lasso, Causal forest, Causal boosting, Local Interpretable Model-Agnostic Explanation (LIME), Passive Aggressive Regressor, Linear Regression, Box-Cox, K-NN, Multilayer perceptron, Ridge, Elastic Net, RidgeCV, Least Angle Regression, Extra Trees, AdaBoost, Gradient Boosting, Failing rule (decision tree) Stacking, SHAP
	Agricultural Risk	P. Feng et al. (2019), Porfirio et al. (2017), Dhokley et al. (2018), Tidake et al. (2020), Ben Ayed and Hanana (2021), Talukdar et al. (2022), Ghaffarian et al. (2022), Liu and Zhan (2019), Coca-Castro, Golden, and Reymondin (n.d.), Gümüşçü, Tenekeci, and Bilgili (2020), Vishwakarma (2019), Belhadi et al. (2021), Sabu and Kumar (2020), S.S. Paul et al. (2020), Cortés and López-Hernández (2021), Müller et al. (2016), Haro et al. (2021)	Random forest, SVM, C4.5 classifier, Decision Trees, Gradient Boosting, Random forest, Multi-layer Perceptron (deep learning), SVM, Logit Boosting, Rotation Forest, Genetic algorithm, Multiple linear regression, Bayesian network, Convolutional Neural Network, Least-squares SVM, Extreme machine learning (feed-forward neural network), Ensemble model, LSTM (Recurrent Neural Networks), K-NN, ANN, Fuzzy logic, K-means, Generalized Boosted Regression, AdaBoost, Gradient Boosting Machines, Radial Basis Function Neural Network, Bagging, Boosting

Table A4. Corpus of documents. ML methods, by topic (Transition Risk).

Application domain		List of papers	List of ML models
Transition Risks	Carbon Markets	Zhu and Chevallier (2017), Zhou, Yu, and Yuan (2018), Levi (2021), Reiersen et al. (2021), Qi et al. (2021), Morkner et al. (2022), Reed, O'Reilly, and Hall (2019), C. Sun (2022), Y. Shi, Shen, and Wu (2021), Biesbroek, Badloe, and Athanasiadis (2020), Kulkarni (2021), Debnath and Bardhan (2020), Donner, Kandlikar, and Webber (2016), Nay (2016), Nay (2017), Pincet, Okabe, and Pawelczyk (2019), X. Feng et al. (2021), Q. Li et al. (2020), Jaycocks (2019), Schmidt et al. (2021), Abdullah et al. (2021), Caldecott et al. (2018), Nguyen, Diaz-Rainey, and Kuruppuarachchi (2021), Han et al. (2021), Yao and Zhao (2022), Khan and Awasthi (2019), M. Li et al. (2021), Fang et al. (2021), Debone, Leite, and Miraglia (2021), Nunnari et al. (2004), Acheampong and Boateng (2019), N. Ma et al. (2021), Q. Sun et al. (2021), Calvo-Pardo, Mancini, and Olmo (2022), Wei, Yuwei, and Chongchong (2018), X. Shi et al. (2020), Rahman, Manini, and Fatima (2021)	Least Squares Support Vector Machines, Extreme learning machine (Deep learning), SVM, Natural language processing (NLP), Back-propagation Neural Network, OLS, Lasso, Genetic Algorithm, ANN, Random Forest, Decision Tree, Convolutional Neural Networks, Multiple Linear regression, OLS, Elastic Net, K-NN, Random forest, Extreme Gradient Boosting Decision Tree, Fuzzy logic, Multilayer perceptron, Multinomial logistic regression, Ensemble model, Convolutional-Long Short Term Memory, Artificial neural network with backpropagation, Gaussian Process Regression, Feed-forward neural network, Extreme machine learning, Lasso, Natural Language Processing (NLP), Latent Dirichlet Allocation (LDA), Machine-coding (Symbolic AI), Fuzzy comprehensive evaluation model, GAN
	Climate data	Diggelmann et al. (2020), Schwabe, Sandhu, and Grebenshchikov (n.d.), Nugent, Stelea, and Leidner (2020), Owusu (2020), Sautner et al. (2020), K. Li and Yu (2022), Antoncic (2020), Kheradmand et al. (n.d.), Luccioni and Palacios (2019), Moreno and Caminero (2022b), Friederich et al. (2021), Luccioni, Baylor, and Duchene (2020), Cojoianu et al. (2020), Miglionico (2022), Raghupathi, Ren, and Raghupathi (2020), Bingler et al. (2022), Benites-Lazaro, Giatti, and Giarolla (2018), Raman, Bang, and Nourbakhsh (2020), Bala et al. (2015), Moreno and Caminero (2022a), Amel-Zadeh et al. (2021), Clarkson et al. (2020), Ehrhardt and Nguyen (2021), Wen (2018), Mansouri and Momtaz (2021)	Natural language Processing (NLP), Natural language understanding (NLU), Context-based algorithms, Keyword discovery algorithm, LDA, Word2vec, Doc2Vec (word embeddings), Text mining, Automated language systems, Text analytics, ClimateBert, Neural language modeling, SVM, Fully-connected neural network, Computer-based textual analysis, Logistic classifier, Lasso, Joint entity, Relation extraction, ANN

Table A5. Corpus of documents. ML methods, by topic (CSR).

Application domain		List of papers	List of ML models
Corporate & Social Responsibility	ESG factors & Investing	Engle et al. (2020), Hilario-Caballero et al. (2020), B. Yu et al. (2022), Lanza, Bernardini, and Faiella (2020), Jha (2021), Margot et al. (2021), Klusak et al. (2021), Vo et al. (2019), Guo et al. (n.d.), Q. Chen and Liu (2020), Erhardt (2020), J. Zhang and Chen (2021), Sokolov et al. (2021), G. Yu et al. (2022), Bua et al. (2022), Cepni, Demirer, and Rognone (2022), Plakandaras, Gogas, and Papadimitriou (2018), Taleb et al. (2020), Tiwari et al. (2022), Hisano, Sornette, and Mizuno (2020), Drei et al. (2019), Chang et al. (2021), Coqueret et al. (2021), Škapa et al. (2023), De Lucia, Pazienza, and Bartlett (2020), Teoh et al. (2019), Sokolov et al. (2020), Mitsuzuka, Ling, and Ohwada (2017), Gupta, Sharma, and Gupta (2021), Sokolov et al. (2021), Krappel, Bogun, and Borth (2021), D'Amato, D'Ecclesia, and Levantesi (2022), Svanberg et al. (2022), Lin and Bai (2022), Bouyé and Menville (2020), Berg et al. (2021), Citterio (n.d.), Kluza, Ziolo, and Spoz (2021), Natsume and Feng (2019), Z. Ma (2019), Anders (2021), Yan and Meng (2021), Joshi and Chauhan (2024), Michalski and Low (2021), Dudás and Naffa (2020), Riad et al. (2019), Hong et al. (2022), Sharma, Gupta, and Gupta (2024)	Textual analysis, Genetic algorithm, Multiobjective evolutionary algorithms, Classification and Regression Trees, Random forest, ANN, SVM, Decision Trees, Support Vector Regression (SVR), Deterministic ML (Symbolic AI), Multivariate Bidirectional Long Short-Term Memory neural network, Deep reinforcement learning, Deep learning, Ensemble model, XGBoost, Fuzzy reasoning, K-NN, AdaBoost, OLS, Lasso, Elastic Net, PLS, Ordered Logistic regression, Ridge, K-NN, SVM, Naive Bayesian, Multilayer Perceptron (MLP) Neural Networks, Long Short-Term Memory (LSTM) Neural Networks, Natural language processing (NLP), Extremely randomized trees, Linear regression, Feed-forward neural network, AdaBoost, CatBoost, XGBoost, Ensemble model, Kohonen neural network, Naïve Bayes, Gradient boosting, Logistic regression, Radial basis function (RBF), SVM, SHAP, Classification tree, Lasso, SHAP
	Climate data	Diggelmann et al. (2020), Schwabe, Sandhu, and Grebenshchikov (n.d.), Nugent, Stelea, and Leidner (2020), Owusu (2020), Sautner et al. (2020), K. Li and Yu (2022), Antoncic (2020), Kheradmand et al. (n.d.), Luccioni and Palacios (2019), Moreno and Caminero (2022b), Friederich et al. (2021), Luccioni, Baylor, and Duchene (2020), Cojoianu et al. (2020), Miglionico (2022), Raghupathi, Ren, and Raghupathi (2020), Bingler et al. (2022), Benites-Lazaro, Giatti, and Giarolla (2018), Raman, Bang, and Nourbakhsh (2020), Bala et al. (2015), Moreno and Caminero (2022a), Amel-Zadeh et al. (2021), Clarkson et al. (2020), Ehrhardt and Nguyen (2021), Wen (2018), Mansouri and Momtaz (2021)	Natural language Processing (NLP), Natural language understanding (NLU), Context-based algorithms, Keyword discovery algorithm, LDA, Word2vec, Doc2Vec (word embeddings), Text mining, Automated language systems, Text analytics, ClimateBert, Neural language modeling, SVM, Fully-connected neural network, Computer-based textual analysis, Logistic classifier, Lasso, Joint entity, Relation extraction, ANN

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