Hugging Face's logo

Datasets:
danielrosehill
/
ifvi_valuefactors_deriv

Languages:
English
Dataset card Files Community
4
danielrosehill commited on
Commit
2d1fc7e
·
1 Parent(s): d78cd91

updated

Browse files
Files changed (2) hide show
  1. README.md +343 -24
  2. docs/documentation/data-dictionary.md +0 -0
README.md CHANGED
@@ -1,42 +1,77 @@
 
 
 
1
  ---
2
  language:
3
  - en
4
  pretty_name: IFVI Value Factors - Derivative Dataset For Analysis
5
-
6
  ---
7
 
8
- ![alt text](images/banner.webp)
9
-
10
- # What if companies' environmental impacts could be quantified in monetary terms!?
11
-
12
- ## About The Global Value Factors Explorer Dataset
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
13
 
14
  The Global Value Factors Database, released by the [International Foundation for Valuing Impacts](https://www.ifvi.org) during UN Climate Week NYC 2023, provides a set of almost 100,000 “value factors” for converting environmental impacts into monetary terms.
15
 
16
  The GVFD covers 430 different environmental impacts across four main categories of impact: air pollution, land use and conversion, waste and water pollution . With the exception of the value factor for greenhouse gas emissions, for which a single value factor is provided ($236/tco2e), the value factors are geographically stratified (in other words, the value factors are both impact-specific and geolocation-specific). In total, there are 268 geolocations in the dataset reflecting all the world's recognised sovereigns as well as some international dependencies. In addition, one set of value factors, air pollution, provides data at the level of US states.
17
 
18
- ## Links to composite files
 
19
 
20
  The following links are to concatenated composite versions of the dataset. These are essentially aggregated versions of the data, encompassing all the various parameters across the different methodologies
21
 
22
  | Format | Link |
23
  |---------|------|
24
- | CSV | ![CSV](https://img.shields.io/badge/Link-CSV-blue?style=flat-square) |
25
- | | [CSV File](https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/blob/main/data/composite-data/all-formats/composite_value_factors.csv) |
26
- | JSON | ![JSON](https://img.shields.io/badge/Link-JSON-blue?style=flat-square) |
27
- | | [JSON File](https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/blob/main/data/composite-data/all-formats/composite_value_factors.json) |
28
- | Parquet | ![Parquet](https://img.shields.io/badge/Link-Parquet-blue?style=flat-square) |
29
- | | [Parquet File](https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/blob/main/data/composite-data/all-formats/composite_value_factors.parquet) |
30
-
31
 
32
  The value factors are intended for use by account preparers preparing financial statements which integrate their environmental and social impacts alongside their traditional financial impacts, unifying all their holistic impacts into one set of financial calculations While the GVFD covers only environmental factors, a key part of the IFVI's mission is also developing methodologies for quantifying social impacts.
33
 
34
  The main categories of impact covered in the dataset are as follows:
35
 
36
- - **Air pollution** provides a set of value factors to monetise the costs associated with six air pollutants linked to effects upon population health, agricultural productivity and other impacts.
37
- - **Land use and conversion** provides monetary impacts for companies' roles in displacing “pristine land” for their operations.
38
- - **Waste** attempts to quantify the impacts generated by companies through both their generation and disposal waste.
39
- - **Water impacts** monetize both companies' **water consumption and their pollutants** covering 104 individual water pollutants.
 
 
40
 
41
  In order to fulfill their intended purpose, the value factors need to be matched with the raw quantitative environmental data which each value factor is intended to convert into monetary terms (the value factors are expressed as conversions to the US dollar).
42
 
@@ -48,15 +83,16 @@ The CSV files should provide the flexibility for users to work with the data as
48
 
49
  Use of the value factors is governed in accordance with the licensing terms provided by the IFVI (which, at the time of writing, provide for free usage for individual account preparers and non-commercial users.) Those looking to read the full official licence should refer to the website of the IFVI at www.ifvi.org
50
 
51
- ## Licensing
52
 
53
  This derivative dataset is subject to the same terms of use as the original database, available in `license.md` at the repository root.
54
 
55
- ## Versioning
56
 
57
  This repository reflects GVFD Version 1 (October 15th, 2024). It is not guaranteed to be the most recent version. Consult the IFVI website for the latest data and updates. While this repository aims to mirror the original GVFD, using this data for official purposes requires referencing the complete IFVI documentation, which is not included here.
58
 
59
- ## Data Formatting
 
60
 
61
  The source data has been restructured for various analytical perspectives:
62
 
@@ -69,15 +105,298 @@ Additional resources:
69
  * CSV format data.
70
  * `metadata/` folder containing non-data items (e.g., notes from the original database tabs).
71
 
72
- ---
73
-
74
- ## Data Modifications
75
 
76
  No material data changes were made. Modifications are limited to formatting and restructuring for analysis. Two non-material changes (documented in the changelog) are:
77
 
78
  * Removal of US dollar signs for easier database integration.
79
  * Standardization of 12 country names to more common versions (e.g., "Bahamas, The" to "Bahamas") and mapping all territories to their ISO-3166 Alpha-2 codes for clarity.
80
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
81
  ---
82
 
83
  ## Author (Source Database / GVFD)
 
1
+ Okay, I understand. I'll revise the previous response to convert descriptions of impact categories into tables. This should make it easier to read and compare the different categories. Here's the updated README:
2
+
3
+ ```markdown
4
  ---
5
  language:
6
  - en
7
  pretty_name: IFVI Value Factors - Derivative Dataset For Analysis
 
8
  ---
9
 
10
+ <p align="center">
11
+ <img src="images/banner.webp" alt="IFVI Banner" width="80%"/>
12
+ </p>
13
+
14
+ # IFVI Value Factors - Derivative Dataset For Analysis
15
+
16
+ <div align="center">
17
+ <a href="#about-the-global-value-factors-explorer-dataset">
18
+ <button>About the Dataset</button>
19
+ </a>
20
+ <a href="#links-to-composite-files">
21
+ <button>Composite Files</button>
22
+ </a>
23
+ <a href="#data-formatting">
24
+ <button>Data Formatting</button>
25
+ </a>
26
+ <a href="#data-modifications">
27
+ <button>Data Modifications</button>
28
+ </a>
29
+ <a href="#release-notes-for-v2">
30
+ <button>Release Notes (V2)</button>
31
+ </a>
32
+ <a href="#specifications">
33
+ <button>Specifications</button>
34
+ </a>
35
+ <a href="#sample-data">
36
+ <button>Sample Data</button>
37
+ </a>
38
+ <a href="#author-source-database--gvfd">
39
+ <button>Authors</button>
40
+ </a>
41
+ </div>
42
+
43
+ <br/>
44
+
45
+ ## 🚀 What if companies' environmental impacts could be quantified in monetary terms!?
46
+
47
+ <a id="about-the-global-value-factors-explorer-dataset"></a>
48
+ ## 🌍 About The Global Value Factors Explorer Dataset
49
 
50
  The Global Value Factors Database, released by the [International Foundation for Valuing Impacts](https://www.ifvi.org) during UN Climate Week NYC 2023, provides a set of almost 100,000 “value factors” for converting environmental impacts into monetary terms.
51
 
52
  The GVFD covers 430 different environmental impacts across four main categories of impact: air pollution, land use and conversion, waste and water pollution . With the exception of the value factor for greenhouse gas emissions, for which a single value factor is provided ($236/tco2e), the value factors are geographically stratified (in other words, the value factors are both impact-specific and geolocation-specific). In total, there are 268 geolocations in the dataset reflecting all the world's recognised sovereigns as well as some international dependencies. In addition, one set of value factors, air pollution, provides data at the level of US states.
53
 
54
+ <a id="links-to-composite-files"></a>
55
+ ## 🔗 Links to Composite Files
56
 
57
  The following links are to concatenated composite versions of the dataset. These are essentially aggregated versions of the data, encompassing all the various parameters across the different methodologies
58
 
59
  | Format | Link |
60
  |---------|------|
61
+ | CSV | [![CSV](https://img.shields.io/badge/Link-CSV-blue?style=flat-square)](https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/blob/main/data/composite-data/all-formats/composite_value_factors.csv) |
62
+ | JSON | [![JSON](https://img.shields.io/badge/Link-JSON-blue?style=flat-square)](https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/blob/main/data/composite-data/all-formats/composite_value_factors.json) |
63
+ | Parquet | [![Parquet](https://img.shields.io/badge/Link-Parquet-blue?style=flat-square)](https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv/blob/main/data/composite-data/all-formats/composite_value_factors.parquet) |
 
 
 
 
64
 
65
  The value factors are intended for use by account preparers preparing financial statements which integrate their environmental and social impacts alongside their traditional financial impacts, unifying all their holistic impacts into one set of financial calculations While the GVFD covers only environmental factors, a key part of the IFVI's mission is also developing methodologies for quantifying social impacts.
66
 
67
  The main categories of impact covered in the dataset are as follows:
68
 
69
+ | Impact Category | Description |
70
+ |------------------------|----------------------------------------------------------------------------------------------------------------------------------------|
71
+ | **Air Pollution** | Provides value factors to monetize the costs of six air pollutants linked to health, agriculture, and other impacts. |
72
+ | **Land Use and Conversion**| Provides monetary impacts for companies' roles in displacing “pristine land” for their operations. |
73
+ | **Waste** | Quantifies impacts from waste generation and disposal. |
74
+ | **Water Impacts** | Monetizes both water consumption and pollution, covering 104 individual water pollutants. |
75
 
76
  In order to fulfill their intended purpose, the value factors need to be matched with the raw quantitative environmental data which each value factor is intended to convert into monetary terms (the value factors are expressed as conversions to the US dollar).
77
 
 
83
 
84
  Use of the value factors is governed in accordance with the licensing terms provided by the IFVI (which, at the time of writing, provide for free usage for individual account preparers and non-commercial users.) Those looking to read the full official licence should refer to the website of the IFVI at www.ifvi.org
85
 
86
+ ## 📜 Licensing
87
 
88
  This derivative dataset is subject to the same terms of use as the original database, available in `license.md` at the repository root.
89
 
90
+ ## 📅 Versioning
91
 
92
  This repository reflects GVFD Version 1 (October 15th, 2024). It is not guaranteed to be the most recent version. Consult the IFVI website for the latest data and updates. While this repository aims to mirror the original GVFD, using this data for official purposes requires referencing the complete IFVI documentation, which is not included here.
93
 
94
+ <a id="data-formatting"></a>
95
+ ## 🗂️ Data Formatting
96
 
97
  The source data has been restructured for various analytical perspectives:
98
 
 
105
  * CSV format data.
106
  * `metadata/` folder containing non-data items (e.g., notes from the original database tabs).
107
 
108
+ <a id="data-modifications"></a>
109
+ ## 🛠️ Data Modifications
 
110
 
111
  No material data changes were made. Modifications are limited to formatting and restructuring for analysis. Two non-material changes (documented in the changelog) are:
112
 
113
  * Removal of US dollar signs for easier database integration.
114
  * Standardization of 12 country names to more common versions (e.g., "Bahamas, The" to "Bahamas") and mapping all territories to their ISO-3166 Alpha-2 codes for clarity.
115
 
116
+ <a id="release-notes-for-v2"></a>
117
+ ## 📝 Release Notes For V2
118
+ ---
119
+ title: "Release Notes: V2, Derivative Dataset, IFVI GVFD"
120
+ ---
121
+
122
+ This release standardises versioning for an early iteration (V2) of the derivative database of the [IFVI Global Value Factors Database (GVFD)](https://ifvi.org/methodology/environmental-topic-methodology/interim-methodologies/).
123
+
124
+ This package consists of `JSON` representations of the original `XLSM` database contained in the original IFVI data release.
125
+
126
+ ### JSON hierarchies reflecting different organisations of the source data
127
+
128
+ The data tables in this derivative dataset are organised into various hierarchies to support different data analytics and visualisation use-cases:
129
+
130
+ - `by-methodology` This folder is divided into subfolders tracking the various methodologies used by the IFVI. The files it contains are "custom" (original) hierarchies representing the data. Not all the methodologies have data tables in this folder.
131
+ - `by-methodology-by-country` This folder maps most closely onto the original format in which the data was released and divides the database firstly by methodology and then by country (and then with impacts, values, etc)
132
+ - `by-territory` This folder consists of individual JSON files for the various countries and territories (including US states) that were included in some or all of the methodology data releases. The datasets here are organised firstly into geographical continents and then by country (or territory; some of the territories are not widely recognised as independent sovereigns). US states - which were included in one methodology - have their own subfolder.
133
+
134
+ ### Data Modifications (Non-Substantive)
135
+
136
+ This dataset (and the repository containing it) is a non-official derivative of the International Foundation for Valuing Impact (IFVI) Global Value Factors Database (GVFD) V1. This derivative dataset is intended to support the programmatic use of the Database for research-related analysis and visualisation.
137
+
138
+ This dataset intends to reflect an accurate reformatting of the source data at the time of its compilation. This version of the derivative dataset is based upon the first version of the GVFD as published by the IFVI on October 15th 2024.
139
+
140
+ No material edits have been made to the source data.
141
+
142
+ The following edits were made solely to support the intended use-case:
143
+
144
+ ---
145
+
146
+ #### Removal of currency symbols
147
+
148
+ To streamline intake of these `JSON` files into database systems, non-integer data (currency symbols) were scrubbed from the dataset. As is noted in the metadata, the IFVI Database is standardised on the US Dollar.
149
+
150
+ #### Editing of country and territory names
151
+
152
+ To assist with geovisualisation use-cases, all countries and territories were matched with their corresponding `alpha-2` values as defined by `ISO 3166`,
153
+
154
+ In order to render the names of countries and territories in more easily recognisable formatting, the names of 18 countries and territories were lightly reformatted.
155
+
156
+ For example `"Bahamas, The"` was renamed `"Bahamas"` and `"Egypt, Arab Rep."` was renamed as simply `"Egypt."`
157
+
158
+ ---
159
+
160
+ ### Separation Of Non-Data Entities
161
+
162
+ - `metadata` This folder provides individual JSONs which capture the notes that were appended on each tab of the source `XLSM`
163
+ - `reference` A static snapshot of the supporting documentation (methodologies and user manuals) released by the IFVI alongside the data release
164
+
165
+ <a id="specifications"></a>
166
+ ## 📊 Specifications
167
+
168
+ <a id="aggregated-data-parameters"></a>
169
+ ### Aggregated Data Parameters
170
+
171
+ #### Air Pollution: Data Description
172
+
173
+ | **Title** | **Details** |
174
+ |-------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
175
+ | **Dataset Name** | Air Pollution Methodology |
176
+ | **Methodology Status** | Interim |
177
+ | **Location-sensitive?** | Yes |
178
+ | **Territories provided**| 197 countries, 51 US states/territories (including Washington, D.C.) |
179
+ | **Example parameters** | PM2.5, PM10, SOx, NOx, NH3, VOC |
180
+ | **Units** | Metric tons per year (per pollutant) |
181
+ | **Sample datapoint** | Air Pollution_PM2.5_Urban_Primary Health |
182
+
183
+ #### GHG Emissions: Data Description
184
+
185
+ | **Title** | **Details** |
186
+ |-------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
187
+ | **Dataset Name** | GHG Methodology |
188
+ | **Methodology Status** | Interim |
189
+ | **Location-sensitive?** | No |
190
+ | **Territories provided**| N/A |
191
+ | **Example parameters** | Global warming potential, carbon dioxide equivalency |
192
+ | **Units** | $/tCO2e (USD per metric ton of CO2 equivalent) |
193
+ | **Sample datapoint** | 236.0 $/tCO2e |
194
+
195
+ #### Land Conversion: Data Description
196
+
197
+ | **Title** | **Details** |
198
+ |-------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
199
+ | **Dataset Name** | Land Conversion Methodology |
200
+ | **Methodology Status** | Interim |
201
+ | **Location-sensitive?** | Yes |
202
+ | **Territories provided**| 197 countries |
203
+ | **Example parameters** | Wheat - conventional, Oilseeds - conventional, Cashmere - sustainable, Forestry, Paved |
204
+ | **Units** | Hectares (for land use categories) |
205
+ | **Sample datapoint** | Land Conversion_Wheat - conventional_Lost Ecosystem Services |
206
+
207
+ #### Land Use: Data Description:
208
+
209
+ | **Title** | **Details** |
210
+ |-------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
211
+ | **Dataset Name** | Land Use Methodology |
212
+ | **Methodology Status** | Interim |
213
+ | **Location-sensitive?** | Yes |
214
+ | **Territories provided**| 197 countries |
215
+ | **Example parameters** | Wheat - conventional, Oilseeds - conventional, Cashmere - sustainable, Forestry, Paved |
216
+ | **Units** | Hectares (ha) |
217
+ | **Sample datapoint** | Land Use_Wheat - conventional_Lost Ecosystem Services |
218
+
219
+ #### Waste: Data Description
220
+
221
+ | **Title** | **Details** |
222
+ |-------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
223
+ | **Dataset Name** | Waste Methodology |
224
+ | **Methodology Status** | Interim |
225
+ | **Location-sensitive?** | Yes |
226
+ | **Territories provided**| 197 countries |
227
+ | **Example parameters** | Hazardous, Non-Hazardous; disposal methods: Landfill, Incineration, Unspecified |
228
+ | **Units** | Kilograms (kg) |
229
+ | **Sample datapoint** | Waste_Hazardous_Landfill_Leachate |
230
+
231
+ #### Water Consumption: Data Description:
232
+
233
+ | **Title** | **Details** |
234
+ |-------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
235
+ | **Dataset Name** | Water Consumption Methodology |
236
+ | **Methodology Status** | Interim |
237
+ | **Location-sensitive?** | No |
238
+ | **Territories provided**| 197 countries |
239
+ | **Example parameters** | Malnutrition, Water-borne disease, Resource cost, Ecosystem services |
240
+ | **Units** | Cubic meters (m³) |
241
+ | **Sample datapoint** | Water Consumption_N/A for WC_N/A for WC_Malnutrition |
242
+
243
+ #### Water Pollution: Data Description:
244
+
245
+ | **Title** | **Details** |
246
+ |-------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
247
+ | **Dataset Name** | Water Pollution Methodology |
248
+ | **Methodology Status** | Interim |
249
+ | **Location-sensitive?** | Yes |
250
+ | **Territories provided**| 197 countries |
251
+ | **Example parameters** | Phosphorus, Nitrogen, Heavy Metals (e.g., Cadmium, Lead, Mercury), Pesticides, Pharmaceuticals (e.g., Antibiotics, NSAIDs) |
252
+ | **Units** | Kilograms (kg) |
253
+ | **Sample datapoint** | Water Pollution_Phosphorus_N/A for this Category_Eutrophication |
254
+
255
+
256
+ <a id="sample-data"></a>
257
+ ## 🧪 Sample Data
258
+
259
+ ### Air Pollution
260
+
261
+ ```csv
262
+ Country,Category,Location,Impact,Units,Reference,Value
263
+ Afghanistan,PM2.5,Urban,Primary Health,/metric ton,Air Pollution_PM2.5_Urban_Primary Health,"40,495.28"
264
+ Afghanistan,PM2.5,Peri-Urban,Primary Health,/metric ton,Air Pollution_PM2.5_Peri-Urban_Primary Health,"34,468.58"
265
+ Afghanistan,PM2.5,Rural,Primary Health,/metric ton,Air Pollution_PM2.5_Rural_Primary Health,"19,386.52"
266
+ Afghanistan,PM2.5,Transport,Primary Health,/metric ton,Air Pollution_PM2.5_Transport_Primary Health,"31,346.36"
267
+ Afghanistan,PM2.5,N/A for PM2.5,Visibility,/metric ton,Air Pollution_PM2.5_N/A for PM2.5_Visibility,4.78
268
+ Afghanistan,SOx,Urban,Primary Health,/metric ton,Air Pollution_SOx_Urban_Primary Health,"13,398.15"
269
+ Afghanistan,SOx,Peri-Urban,Primary Health,/metric ton,Air Pollution_SOx_Peri-Urban_Primary Health,"13,345.45"
270
+ Afghanistan,SOx,Rural,Primary Health,/metric ton,Air Pollution_SOx_Rural_Primary Health,"6,694.38"
271
+ Afghanistan,SOx,Transport,Primary Health,/metric ton,Air Pollution_SOx_Transport_Primary Health,"10,893.71"
272
+ Afghanistan,SOx,N/A for SOx,Visibility,/metric ton,Air Pollution_SOx_N/A for SOx_Visibility,31.86
273
+ Afghanistan,NH3,Urban,Primary Health,/metric ton,Air Pollution_NH3_Urban_Primary Health,"12,148.59"
274
+ Afghanistan,NH3,Peri-Urban,Primary Health,/metric ton,Air Pollution_NH3_Peri-Urban_Primary Health,"10,340.57"
275
+ Afghanistan,NH3,Rural,Primary Health,/metric ton,Air Pollution_NH3_Rural_Primary Health,"5,815.95"
276
+ Afghanistan,NH3,Transport,Primary Health,/metric ton,Air Pollution_NH3_Transport_Primary Health,"9,403.91"
277
+ Afghanistan,NH3,N/A for NH3,Visibility,/metric ton,Air Pollution_NH3_N/A for NH3_Visibility,6.06
278
+ Afghanistan,PM10,Urban,Primary Health,/metric ton,Air Pollution_PM10_Urban_Primary Health,260.51
279
+ Afghanistan,PM10,Peri-Urban,Primary Health,/metric ton,Air Pollution_PM10_Peri-Urban_Primary Health,238.92
280
+ Afghanistan,PM10,Rural,Primary Health,/metric ton,Air Pollution_PM10_Rural_Primary Health,120.84
281
+ ```
282
+
283
+ ### Land Conversion
284
+
285
+ ```
286
+ Country,Category,Location,Impact,Units,Reference,Value
287
+ Afghanistan,Wheat - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Wheat - conventional_N/A for LULC_Lost Ecosystem Services,"12,573.76"
288
+ Afghanistan,"Vegetables, fruit, nuts - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Conversion_Vegetables, fruit, nuts - conventional_N/A for LULC_Lost Ecosystem Services","14,424.09"
289
+ Afghanistan,"Cereals, grains - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Conversion_Cereals, grains - conventional_N/A for LULC_Lost Ecosystem Services","12,573.76"
290
+ Afghanistan,Oilseeds - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Oilseeds - conventional_N/A for LULC_Lost Ecosystem Services,"12,573.76"
291
+ Afghanistan,"Sugarcane, sugarbeet - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Conversion_Sugarcane, sugarbeet - conventional_N/A for LULC_Lost Ecosystem Services","12,573.76"
292
+ Afghanistan,Plant-based fibers - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Plant-based fibers - conventional_N/A for LULC_Lost Ecosystem Services,"12,573.76"
293
+ Afghanistan,Other crops - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Other crops - conventional_N/A for LULC_Lost Ecosystem Services,"12,573.76"
294
+ Afghanistan,Other crops - organic,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Other crops - organic_N/A for LULC_Lost Ecosystem Services,"11,640.73"
295
+ Afghanistan,Other crops - sustainable,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Other crops - sustainable_N/A for LULC_Lost Ecosystem Services,"10,870.67"
296
+ Afghanistan,"Bovine, sheep, goats, horses - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Conversion_Bovine, sheep, goats, horses - conventional_N/A for LULC_Lost Ecosystem Services","14,200.25"
297
+ Afghanistan,"Bovine, sheep, goats, horses - organic",N/A for LULC,Lost Ecosystem Services,/ha,"Land Conversion_Bovine, sheep, goats, horses - organic_N/A for LULC_Lost Ecosystem Services","13,676.30"
298
+ Afghanistan,"Bovine, sheep, goats, horses - sustainable",N/A for LULC,Lost Ecosystem Services,/ha,"Land Conversion_Bovine, sheep, goats, horses - sustainable_N/A for LULC_Lost Ecosystem Services","13,521.12"
299
+ Afghanistan,Cashmere - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Cashmere - conventional_N/A for LULC_Lost Ecosystem Services,"14,724.20"
300
+ Afghanistan,Cashmere - organic,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Cashmere - organic_N/A for LULC_Lost Ecosystem Services,"13,676.30"
301
+ Afghanistan,Cashmere - sustainable,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Cashmere - sustainable_N/A for LULC_Lost Ecosystem Services,"13,521.12"
302
+ Afghanistan,Forestry,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Forestry_N/A for LULC_Lost Ecosystem Services,"1,441.78"
303
+ Afghanistan,Paddy rice,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Paddy rice_N/A for LULC_Lost Ecosystem Services,"10,984.10"
304
+ ```
305
+
306
+ ### Land Use
307
+
308
+ ```
309
+ Country,Category,Location,Impact,Units,Reference,Value
310
+ Afghanistan,Wheat - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Wheat - conventional_N/A for LULC_Lost Ecosystem Services,216.64
311
+ Afghanistan,"Vegetables, fruit, nuts - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Use_Vegetables, fruit, nuts - conventional_N/A for LULC_Lost Ecosystem Services",248.52
312
+ Afghanistan,"Cereals, grains - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Use_Cereals, grains - conventional_N/A for LULC_Lost Ecosystem Services",216.64
313
+ Afghanistan,Oilseeds - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Oilseeds - conventional_N/A for LULC_Lost Ecosystem Services,216.64
314
+ Afghanistan,"Sugarcane, sugarbeet - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Use_Sugarcane, sugarbeet - conventional_N/A for LULC_Lost Ecosystem Services",216.64
315
+ Afghanistan,Plant-based fibers - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Plant-based fibers - conventional_N/A for LULC_Lost Ecosystem Services,216.64
316
+ Afghanistan,Other crops - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Other crops - conventional_N/A for LULC_Lost Ecosystem Services,216.64
317
+ Afghanistan,Other crops - organic,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Other crops - organic_N/A for LULC_Lost Ecosystem Services,200.56
318
+ Afghanistan,Other crops - sustainable,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Other crops - sustainable_N/A for LULC_Lost Ecosystem Services,187.3
319
+ Afghanistan,"Bovine, sheep, goats, horses - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Use_Bovine, sheep, goats, horses - conventional_N/A for LULC_Lost Ecosystem Services",244.66
320
+ Afghanistan,"Bovine, sheep, goats, horses - organic",N/A for LULC,Lost Ecosystem Services,/ha,"Land Use_Bovine, sheep, goats, horses - organic_N/A for LULC_Lost Ecosystem Services",235.64
321
+ Afghanistan,"Bovine, sheep, goats, horses - sustainable",N/A for LULC,Lost Ecosystem Services,/ha,"Land Use_Bovine, sheep, goats, horses - sustainable_N/A for LULC_Lost Ecosystem Services",232.96
322
+ Afghanistan,Cashmere - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Cashmere - conventional_N/A for LULC_Lost Ecosystem Services,253.69
323
+ Afghanistan,Cashmere - organic,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Cashmere - organic_N/A for LULC_Lost Ecosystem Services,235.64
324
+ Afghanistan,Cashmere - sustainable,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Cashmere - sustainable_N/A for LULC_Lost Ecosystem Services,232.96
325
+ Afghanistan,Forestry,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Forestry_N/A for LULC_Lost Ecosystem Services,24.84
326
+ Afghanistan,Paddy rice,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Paddy rice_N/A for LULC_Lost Ecosystem Services,189.25
327
+ Afghanistan,Paved,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Paved_N/A for LULC_Lost Ecosystem Services,312.21
328
+ ```
329
+
330
+ ### Waste
331
+
332
+ ```
333
+ Country,Category,Location,Impact,Units,Reference,Value
334
+ Afghanistan,Hazardous,Landfill,Leachate,/kg,Waste_Hazardous_Landfill_Leachate,18.19
335
+ Afghanistan,Hazardous,Landfill,Waste GHGs,/kg,Waste_Hazardous_Landfill_Waste GHGs,179.15
336
+ Afghanistan,Hazardous,Landfill,Disamenity,/kg,Waste_Hazardous_Landfill_Disamenity,45.96
337
+ Afghanistan,Non-Hazardous,Landfill,Leachate,/kg,Waste_Non-Hazardous_Landfill_Leachate,0.3
338
+ Afghanistan,Non-Hazardous,Landfill,Waste GHGs,/kg,Waste_Non-Hazardous_Landfill_Waste GHGs,179.15
339
+ Afghanistan,Non-Hazardous,Landfill,Disamenity,/kg,Waste_Non-Hazardous_Landfill_Disamenity,45.96
340
+ Afghanistan,Hazardous,Incineration,Waste GHGs,/kg,Waste_Hazardous_Incineration_Waste GHGs,386.36
341
+ Afghanistan,Hazardous,Incineration,Disamenity,/kg,Waste_Hazardous_Incineration_Disamenity,3.01
342
+ Afghanistan,Hazardous,Incineration,Waste Air pollution,/kg,Waste_Hazardous_Incineration_Waste Air pollution,18.28
343
+ Afghanistan,Hazardous,Incineration,Heavy metals and dioxins,/kg,Waste_Hazardous_Incineration_Heavy metals and dioxins,4.93
344
+ Afghanistan,Non-Hazardous,Incineration,Waste GHGs,/kg,Waste_Non-Hazardous_Incineration_Waste GHGs,124.02
345
+ Afghanistan,Non-Hazardous,Incineration,Disamenity,/kg,Waste_Non-Hazardous_Incineration_Disamenity,3.01
346
+ Afghanistan,Non-Hazardous,Incineration,Waste Air pollution,/kg,Waste_Non-Hazardous_Incineration_Waste Air pollution,18.28
347
+ Afghanistan,Non-Hazardous,Incineration,Heavy metals and dioxins,/kg,Waste_Non-Hazardous_Incineration_Heavy metals and dioxins,4.93
348
+ Afghanistan,Hazardous,Unspecified,Leachate,/kg,Waste_Hazardous_Unspecified_Leachate,0.0
349
+ Afghanistan,Hazardous,Unspecified,Waste Air pollution,/kg,Waste_Hazardous_Unspecified_Waste Air pollution,18.28
350
+ Afghanistan,Hazardous,Unspecified,Heavy metals and dioxins,/kg,Waste_Hazardous_Unspecified_Heavy metals and dioxins,4.93
351
+ Afghanistan,Hazardous,Unspecified,Disamenity,/kg,Waste_Hazardous_Unspecified_Disamenity,3.01
352
+ Afghanistan,Hazardous,Unspecified,Waste GHGs,/kg,Waste_Hazardous_Unspecified_Waste GHGs,386.36
353
+ Afghanistan,Non-Hazardous,Unspecified,Leachate,/kg,Waste_Non-Hazardous_Unspecified_Leachate,0.3
354
+ Afghanistan,Non-Hazardous,Unspecified,Waste Air pollution,/kg,Waste_Non-Hazardous_Unspecified_Waste Air pollution,0.0
355
+ Afghanistan,Non-Hazardous,Unspecified,Heavy metals and dioxins,/kg,Waste_Non-Hazardous_Unspecified_Heavy metals and dioxins,0.0
356
+ Afghanistan,Non-Hazardous,Unspecified,Disamenity,/kg,Waste_Non-Hazardous_Unspecified_Disamenity,45.96
357
+ Afghanistan,Non-Hazardous,Unspecified,Waste GHGs,/kg,Waste_Non-Hazardous_Unspecified_Waste GHGs,179.15
358
+ ```
359
+
360
+ ### Water Consumption
361
+
362
+ ```
363
+ Country,Category,Location,Impact,Units,Reference,Value
364
+ Afghanistan,N/A for WC,N/A for WC,Malnutrition,/m3,Water Consumption_N/A for WC_N/A for WC_Malnutrition,0.49
365
+ Afghanistan,N/A for WC,N/A for WC,Water-borne disease,/m3,Water Consumption_N/A for WC_N/A for WC_Water-borne disease,0.06
366
+ Afghanistan,N/A for WC,N/A for WC,Resource cost,/m3,Water Consumption_N/A for WC_N/A for WC_Resource cost,0.32
367
+ Afghanistan,N/A for WC,N/A for WC,Ecosystem services,/m3,Water Consumption_N/A for WC_N/A for WC_Ecosystem services,0.28
368
+ Albania,N/A for WC,N/A for WC,Malnutrition,/m3,Water Consumption_N/A for WC_N/A for WC_Malnutrition,0.02
369
+ Albania,N/A for WC,N/A for WC,Water-borne disease,/m3,Water Consumption_N/A for WC_N/A for WC_Water-borne disease,0.13
370
+ Albania,N/A for WC,N/A for WC,Resource cost,/m3,Water Consumption_N/A for WC_N/A for WC_Resource cost,1.0
371
+ Albania,N/A for WC,N/A for WC,Ecosystem services,/m3,Water Consumption_N/A for WC_N/A for WC_Ecosystem services,1.94
372
+ Algeria,N/A for WC,N/A for WC,Malnutrition,/m3,Water Consumption_N/A for WC_N/A for WC_Malnutrition,0.24
373
+ Algeria,N/A for WC,N/A for WC,Water-borne disease,/m3,Water Consumption_N/A for WC_N/A for WC_Water-borne disease,0.0
374
+ Algeria,N/A for WC,N/A for WC,Resource cost,/m3,Water Consumption_N/A for WC_N/A for WC_Resource cost,0.43
375
+ Algeria,N/A for WC,N/A for WC,Ecosystem services,/m3,Water Consumption_N/A for WC_N/A for WC_Ecosystem services,0.08
376
+ American Samoa,N/A for WC,N/A for WC,Malnutrition,/m3,Water Consumption_N/A for WC_N/A for WC_Malnutrition,0.3
377
+ American Samoa,N/A for WC,N/A for WC,Water-borne disease,/m3,Water Consumption_N/A for WC_N/A for WC_Water-borne disease,0.11
378
+ American Samoa,N/A for WC,N/A for WC,
379
+ ```
380
+
381
+ # Water Pollution
382
+
383
+ ```
384
+ Country,Category,Location,Impact,Units,Reference,Value
385
+ Afghanistan,Phosphorus,N/A for this Category,Eutrophication,/kg,Water Pollution_Phosphorus_N/A for this Category_Eutrophication,96.6218
386
+ Afghanistan,Nitrogen,N/A for this Category,Eutrophication,/kg,Water Pollution_Nitrogen_N/A for this Category_Eutrophication,0.0000
387
+ Afghanistan,Ag(I),Freshwater,Health,/kg,Water Pollution_Ag(I)_Freshwater_Health,41.6088
388
+ Afghanistan,Ag(I),Seawater,Health,/kg,Water Pollution_Ag(I)_Seawater_Health,0.8362
389
+ Afghanistan,Ag(I),Unspecified,Health,/kg,Water Pollution_Ag(I)_Unspecified_Health,41.6088
390
+ Afghanistan,As(III),Freshwater,Health,/kg,Water Pollution_As(III)_Freshwater_Health,"2,018.0068"
391
+ Afghanistan,As(III),Seawater,Health,/kg,Water Pollution_As(III)_Seawater_Health,169.1855
392
+ Afghanistan,As(III),Unspecified,Health,/kg,Water Pollution_As(III)_Unspecified_Health,"2,018.0068"
393
+ Afghanistan,As(V),Freshwater,Health,/kg,Water Pollution_As(V)_Freshwater_Health,"2,018.0068"
394
+ Afghanistan,As(V),Seawater,Health,/kg,Water Pollution_As(V)_Seawater_Health,169.1855
395
+ Afghanistan,As(V),Unspecified,Health,/kg,Water Pollution_As(V)_Unspecified_Health,"2,018.0068"
396
+ Afghanistan,Ba(II),Freshwater,Health,/kg,Water Pollution_Ba(II)_Freshwater_Health,64.0374
397
+ Afghanistan,Ba(II),Seawater,Health,/kg,Water Pollution_Ba(II)_Seawater_Health,12.9373
398
+ ```
399
+
400
  ---
401
 
402
  ## Author (Source Database / GVFD)
docs/documentation/data-dictionary.md DELETED
File without changes