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This article assesses 101 randomly selected companies for their biodiversity-related reporting of environmental performance indicators to meet the Global Reporting Initiative (GRI) G3.1 guideline requirement. To evaluate the reporting of environment performance indicators related to biodiversity, a 1-5 rating scale was developed where 5 ranked the highest. The maximum rating of 5 was obtained by 13% of the reporting companies. According to the GRI G3.1 guideline, environmental performance indicator number 12 (EN12) requests the companies to describe the 'significant impacts of their activities, products, and services on biodiversity in protected areas and areas of high biodiversity value outside protected areas'. Most of the sampled companies (82%) reported this indicator. Environmental performance indicator number 15 (EN15) requests the companies to disclose the 'number of IUCN Red List species and national conservation list species with habitats in areas affected by operations, by level of extinction risk'. This was the least-reported indicator by the surveyed companies (25%). Reporting of environmental performance indicators related to biodiversity, and initiatives based on GRI guidelines have been adopted with varying degrees of success by business organizations, but efforts are still required to understand the returns from the initiatives undertaken and reporting the returns earned. ARTICLE HISTORY

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This paper presents a methodological framework for predicting C stock in Avicennia marina stands in the Thane creek of Mumbai. This methodology combines ground-based (GB) and remote sensing (RS) approaches for C stock estimation. RS based approach use Normalized differential vegetation index (NDVI), Light use efficiency (LUE) and Photo-synthetically Active Radiation (PAR) as the most important parameters for C stock estimation. The sensitivity of NDVI values to aerosols, water vapor and ozone was removed using the 6S radiative transfer code. The difference in NDVI values before and after atmospheric correction was assessed using student's t-test and was found to be statistically significant. The total carbon stock of the area was observed to be about 39.7188 t/ha. The bias estimation between C stock calculated using allometry and RS approaches confirmed the prediction accuracy and validated both the techniques (R 2 = 0.964 and bias=0.915 %). The paper, thus, reports a statistically robust framework, which is a combination of the RS and GB approaches, and can be used for estimating the biomass and carbon stock of any ecosystem. This framework is especially effective where forest inventory data is unavailable, the site is geographically inaccessible or harvesting of mangroves or other trees is prohibited.