Emerging Markets and ESG Data Gaps: How AI/Tech Can Help Measure and Meet ESG Goals in Less Digitised Environments

Emerging markets face significant hurdles in ESG reporting owing to fragmented data and limited digitisation. AI and supporting technologies offer practical solutions to bridge these gaps, enabling accurate measurement and goal achievement. This blog explores the challenges and tech-driven pathways forward.

ESG Data Challenges in Emerging Markets

Emerging markets like those in Southeast Asia, Africa and Latin America struggle with ESG data scarcity. Unlike developed economies with standardised reporting, these regions often lack centralised databases, reliable metrics and regulatory enforcement, leading to incomplete environmental impact assessments or social governance insights. For instance, small enterprises rely on manual records, while supply chains span informal sectors with no digital trails.

This results in "data gaps" that deter investors seeking ESG-compliant opportunities. Asset managers report up to 70% missing data for emerging market issuers, complicating risk assessment for climate vulnerabilities or labour practices. Less digitised environments exacerbate issues, as low internet penetration and legacy systems hinder real-time tracking.

Key Data Gaps Exposed

Gap Type	Description	Impact in Emerging Markets
Environmental	Inaccurate emissions data from agriculture or mining	Undermeasured Scope 3 emissions in supply chains
Social	Limited diversity or community impact metrics	Informal labour sectors evade reporting
Governance	Weak anti-corruption or board diversity records	Opaque ownership in family-run firms
Technological	Low sensor/IoT adoption	No real-time water usage or waste monitoring

These gaps stem from infrastructural limits, not unwillingness. In India or Brazil, for example, rural operations lack digitisation, forcing reliance on estimates prone to error.

AI's Role in Data Collection

AI excels at extracting insights from unstructured sources prevalent in less digitised areas. Natural Language Processing (NLP) scans annual reports, news and local filings to identify ESG terms like "emissions reduction" or "worker safety," filling voids in structured data. Tools like MALENA or ESG NLP process thousands of documents, achieving high accuracy in sentiment analysis for social risks.

Satellite imagery and computer vision monitor deforestation or water pollution without ground sensors. In Indonesia's palm oil sector, AI analyses imagery to estimate carbon footprints, bypassing manual surveys. Machine learning integrates IoT where available, scaling sparse data into predictive models.

Tech Solutions for Measurement

Beyond AI, complementary tech addresses digitisation barriers. Blockchain ensures tamper-proof supply chain tracking, vital for provenance in African mining. Low-cost IoT sensors, powered by edge computing, function offline in remote areas, syncing data upon connectivity.

Technology	Application	Benefit in Low-Digitisation
NLP/ML	Unstructured text analysis	Extracts ESG from local languages/reports
Satellite AI	Environmental monitoring	Covers inaccessible terrains
IoT/Edge	Real-time metrics	Works without constant internet
Blockchain	Data verification	Builds trust in informal chains

Predictive analytics forecast ESG trends, like flood risks from climate data, aiding proactive compliance.

Case Studies from Emerging Markets

In Brazil, AI platforms analyse MDB disclosures and bond prospectuses for financial issuers, uncovering governance insights missed by traditional methods. Amundi and IFC's ESG NLP tool processed unstructured texts, revealing ESG performance patterns across Latin American bonds.

India's asset managers use AI for Scope 3 emissions in textiles, integrating supplier surveys with satellite data for comprehensive reporting. African FinTech's deploy mobile-based AI apps for SME ESG scoring, leapfrogging infrastructure gaps via SMS data inputs. These yield 30-40% faster processing and higher accuracy.

Implementation Strategies

Start with pilot projects targeting high-impact areas like emissions. Cross-functional teams—sustainability, IT, executives—align AI with goals, avoiding silos. Train models on local data for context, such as Hindi/Portuguese NLP for regional nuances.

Partner with providers offering SaaS solutions for scalability. In Nigeria, banks use AI for real-time social metrics from transaction data, meeting CSRD-like standards. Measure success via reduced reporting time (up to 100x faster) and anomaly detection.

Challenges include data privacy and bias; mitigate with domain-specific training and audits.

Regulatory and Investor Pressures

Global mandates like EU's CSRD push emerging markets toward disclosure. Investors demand verifiable ESG data, with AI enabling compliance without heavy infrastructure. President Trump's 2025 policies emphasise sustainable growth, favouring tech-leveraged emerging investments.

AI highlights non-disclosed material info, flagging risks for regulators.

Future Outlook and Opportunities

AI will evolve with multimodal models combining text, images and sensors for holistic ESG views. By 2030, expect 50% gap closure in emerging markets via generative AI drafting reports. Early adopters gain competitive edges, attracting green bonds.

Organisations prioritising AI-ESG integration turn compliance into strategy, fostering innovation like low-carbon products.

In less digitised environments, AI does not replace infrastructure but amplifies sparse data into actionable intelligence. Emerging markets can lead in agile ESG adoption, driving inclusive growth.

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