Investors’ sophistication on climate risk is increasing and as part of this they require high-quality and comprehensive Scope 3 emissions data. Accordingly, we investigate Scope 3 emissions data divergence (across different providers), composition (which Scope 3 categories are reported) and whether machine-learning models can be used to predict Scope 3 emissions for non-reporting firms. We find considerable divergence in theaggregatedScope 3 emissions values from three of the largest data providers (Bloomberg, Refinitiv Eikon, and ISS). The divergence is largest for ISS, as it replaces reported Scope 3 emissions with estimates from its economic input-output and life cycle assessment modelling. With respect to the composition of Scope 3 emissions, firms generally report incomplete composition, yet they are reporting more categories over time. There is a persistent contrast between relevance and completeness in the composition of Scope 3 emissions across sectors, with low materiality categories such as travel emissions being reported more frequently than typically high materiality ones, such as the use of products and processing of sold products. Finally, machine learning algorithms can improve the prediction accuracy of the aggregated Scope 3 emissions by up to 6% and up to 25% when each category is estimated individually and aggregated into total Scope 3 emissions. However, absolute prediction performance is low even with the best models, with the accuracy of estimates primarily limited by low observations in specific Scope 3 categories. We conclude that investors should be cognizant of Scope 3 emissions data divergence, incomplete reporting of Scope 3 categories, and that predictions for non-reporting firms have high absolute errors even when using machine learning models. For both reported and estimated data, caveat emptor applies.

Financing climate change mitigation and adaptation in Asia is critical for its population and economies but also for those of the world. This editorial provides a commentary and overview of ten articles within this special issue of Climate Policy on ‘Green Finance in Asia’. Contributions are diverse in terms of focus and methods. Most of the articles focus on managing transition risk with six of the articles having an energy focus; the dominant themes are the risk of stranded coal assets in China; the role of export finance by China and Japan for fossil fuel-fired power generation assets within Asia; and investment in renewable power generation and the policies to support such investment. The remaining four articles explore various policies in specific country contexts: the effects of green bond policies in China; the greening of monetary policy in China; the governance of sustainable finance in Indonesia; and policies to support investment and finance of off-grid electricity access in Bangladesh. Most of the ten contributions come from researchers in developed countries and principally from non-Asian countries, suggesting there is a need to develop green finance research capability and capacity across Asia. Despite being withing scope of the special issue, this collection does not contain papers on physical risk or adaptation finance. We highlight these as important gaps and priorities for future research. Key policy insights

We take advantage of a combination of a severe weather event from 3 to 4 June 2015 and a local policy, to investigate the housing market response to climate change‐related flooding hazard. The study focuses on a residential area in a low‐lying coastal suburb of Dunedin, New Zealand, where the groundwater level is shallow and close to sea level. An unusually heavy rain event in June 2015 resulted in flooding of a significant portion of land in especially low‐lying areas. The city council responded by reviewing processes for storm‐water management and by imposing minimum‐floor‐level [MFL] requirements on new construction in the low‐lying areas previously identified as at risk of flooding. Applying a ‘diff‐in‐diff‐in‐diff’ strategy in hedonic regression analyses, we find that houses in the MFL zone sell for a discount of about 5 per cent prior to the flood. This discount briefly tripled in the area that flooded, but disappeared within 15 months, indicating either very short memory among homebuyers or no long‐run change in perception of hazard.

We examine climate transition risk in New Zealand (NZ) equities given that NZ’s greenhouse gas (GHG) emissions are dominated by agricultural emissions and that carbon pricing has been in place since 2008. We find disclosure has grown rapidly from 2010 and that disclosure is driven by, inter alia, size and sector membership (Energy, Manufacturing and Primary Industries). However, by 2018, only around half of NZX50 companies regularly disclose emissions, though this should increase steadily as NZ will adopt mandatory disclosures from 2023 onwards on a ‘report or explain basis’. In terms of ‘hypothetical carbon liabilities’, Genesis Energy and Air New Zealand are most exposed for Scope 1 and 2 emissions, but when upstream scope 3 GHG emissions are added, Fonterra (multinational dairy firm) is most at-risk. An asset pricing analysis shows that only volatility and extreme price movements in carbon price returns are priced. Overall, the results suggest that despite there being material climate transition risks for NZX50 equities, limited disclosure and low carbon prices mean that these risks are not likely to be fully priced in stock values.