The current dependence on a linear economy is largely responsible for most impacts on nature and biodiversity. Some 90% of biodiversity loss is caused by the way we extract and process materials, fuels and food (UNEP, 2019). Approximately 60 billion tons of renewable and nonrenewable resources are extracted yearly, a 15% augmentation since the 1980s (IPBES, 2019). Furthermore, following land and sea use change, the largest negative impact on nature is the direct exploitation, especially overexploitation, of natural resources and organisms via harvesting, logging, hunting and fishing (IPBES, 2019). By decoupling economic prosperity from resource consumption and environmental degradation, circularity offers opportunities for new and better growth that not only help safeguard and rebuild biodiversity but also provide benefits.
A note on the scoring system
Wikirate uses a standardized 10-point scoring system to enable comparison of company scores across different benchmarks. In the Nature Benchmark, companies can earn 1 point per indicator, which are then added together and calculated as a percentage of the total score for a measurement area (MA) to determine the final score for the MA. The overall Wikirate score is calculated by combining these scores with the appropriate weightings, and converting them to a 10-point scale. For instance, if a company achieves a final score of 50, the corresponding Rating will be 5.
score = (x) -> if x == "Yes" 10 else if x == "Partially" 5 else 0 redistributed_weights = (weights, values) -> num_of_weighted_values = weights.length for value, index in values if value == "Not Applicable" num_of_weighted_values -= 1 redistributed_weight_value = weights[index] for weight, i in weights if weight == 0 continue weights[i] = weight + redistributed_weight_value/num_of_weighted_values weights[index] = 0 weights weights = [0.1, 0.2, 0.1, 0.2, 0.2, 0.2] resource_exploitation = [evidence, input_disclosure, organism_exploitation, management_plan,reporting, decoupling_finance_and_consumption] redistributed_weights(weights, resource_exploitation) weights.reduce((weighted_sum, weight, index) -> weighted_sum + weight*score(resource_exploitation[index]) , 0)