{ "docId": "019dd923-5e88-73ef-bd59-6131f37efc2b", "docSlug": "71b5b657f034a972", "documentTitle": "ey net zero centre carbon offset publication 20220530", "authorId": "MorganStanley", "authorName": "Grantham Research Institute", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "equity_research", "sourceTypeLabel": "Equity research", "presentationDate": null, "orientation": "portrait", "aspectRatio": 0.707, "pageNumber": 24, "pageCount": 45, "prevPage": 23, "nextPage": 25, "slideType": "appendix_methodology", "function": "diagnose", "density": "overcrowded", "nDataPoints": 0, "notes": "This is a page from an academic or technical report, likely an appendix detailing sensitivity analyses.", "elementsJson": [ "paragraph" ], "metadataConfidence": 0.9, "imagePath": null, "slideHref": "/slides/019dd923-5e88-73ef-bd59-6131f37efc2b/24", "deckHref": "/decks/019dd923-5e88-73ef-bd59-6131f37efc2b", "deckJsonHref": "/decks/019dd923-5e88-73ef-bd59-6131f37efc2b.json", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd59-6131f37efc2b#slide-24", "components": [ { "bbox": { "h": 0.14, "w": 0.78, "x": 0.11, "y": 0.56 }, "kind": "paragraph", "text": "Turning now to our estimates of capacity factors, we consider two possible sources of measurement error—the benchmark turbine and the air density estimation. In our main analysis we use the technical specifications of Enercon's E-53 turbine to estimate capacity factors. This is the most common turbine in our database. The power curve of any particular turbine, however, will undoubtedly favor some wind profiles, i.e. locations, over others. If these locations happen to be correlated with CDM registration, this would bias our results. We address this concern by swapping out Enercon's E-53 turbine for Suzlon's S82 turbine—another very common turbine in our data set that has a different power curve. In row (5) of Table 1 we see that this substitution makes very little difference to the results.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "2af2582b-e775-4749-8eaa-1aeb1aa8c8fc", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.05, "w": 0.78, "x": 0.11, "y": 0.09 }, "kind": "paragraph", "text": "start by re-computing our results using alternative estimates of connection costs and capacity factors. Since each method of estimation is likely to produce different errors, it would be revealing if this exercise yielded substantially different results.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "34e3559e-32a6-4ed5-8dc8-3927c26ba927", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.11, "w": 0.78, "x": 0.11, "y": 0.24 }, "kind": "paragraph", "text": "To address this concern, we re-estimate connection distances while imposing the constraint that wind farms may only be connected to substations in the same state. State boundaries are the most important obstacles preventing wind farms from connecting to the nearest substation, so this should redress any systematic imbalance between CDM and non-CDM projects with respect to the administrative obstacles associated with transecting state boundaries. The results, reported in row (1) of Table 1, are almost identical to the original estimates. If anything, the CDM's performance slightly deteriorates both in absolute terms and relative to a lottery.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "48370331-f029-4d0c-9ca7-c2298372cf13", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.08, "w": 0.78, "x": 0.11, "y": 0.15 }, "kind": "paragraph", "text": "One possible source of asymmetric error in our estimates of connection costs is our assumption that wind farms are always connected to the nearest substation. It might be that CDM projects need extra support because they face greater obstacles to connect to nearby substations and instead can only connect to more distant substations. In this case, we will have systematically underestimated the connection costs of CDM projects relative to non-CDM projects.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "6037d7e8-5b44-465c-a0a6-d1c3c014020d", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.05, "w": 0.78, "x": 0.11, "y": 0.71 }, "kind": "paragraph", "text": "The air density estimates could contribute measurement errors through the same mechanism. Capacity factors are often estimated using data on wind speeds alone, while assuming a standard air density. If our estimates of air density are noisier for some locations than others, and this", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "a2d0b900-aecf-4a3b-bbb4-8d6b5d693687", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.11, "w": 0.78, "x": 0.11, "y": 0.44 }, "kind": "paragraph", "text": "We can examine this hypothesis indirectly by substituting a range of alternative lists of plausible grid connection points. In row (2) of Table 1 we report the results using the locations of conventional power plants. In row (3) we use the location of cities that, according to the 2001 Indian census, had a population of at least 100,000. In row (4) we use the location of cities listed in the 2001 Indian census as having electrical power. The results barely change, indicating either that all four lists suffer from the same exact bias, or that the list compiled by Burlig et al. (2020) does not suffer from systematic omissions.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "bc5d2323-1e2a-43d4-a435-98d1b8f32fb3", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.02, "w": 0.02, "x": 0.49, "y": 0.94 }, "kind": "paragraph", "text": "22", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "d28094cd-9319-4731-94b4-6c908aa4e17c", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.07, "w": 0.78, "x": 0.11, "y": 0.36 }, "kind": "paragraph", "text": "Another possible source of error in our estimates of connection costs comes from the comparative difficulty in obtaining high-quality data on the locations of electrical substations. Our main analysis uses a data set compiled by Burlig et al. (2020). If this list happens to disproportionately miss substations with no CDM project nearby then we would artificially increase connection distances for non-CDM projects.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "f1d14e7e-504a-4970-bfba-5ed4d8128767", "frameworkName": null, "frameworkSlug": null } ], "metrics": [], "tools": [ { "name": "List presentation", "slug": "list-presentation", "agent": null, "layer": "slide", "matchId": "547d9ac4-109f-470a-8182-e8d2e6099d8a", "evidence": "Turning now to our estimates of capacity factors, we consider two possible sources of measurement error—the benchmark turbine and the air density estimation.", "confidence": 0.5 } ], "frameworks": [], "arcBeats": [], "loops": [], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }