DATASET Analytical Data for the Cost-Benefit Impact Assessment of Clean Production Agreements
Description
This dataset was generated for the analysis presented in the article Application of distributional weights for the cost-benefit impact assessment of sustainable production programs. A case study of Clean Production Agreements. It contains the consolidated analytical data for the Clean Production Agreements (CPAs) certified in Chile between 2012 and 2024. The file merges data from multiple public and institutional sources and includes calculated variables used for the distributional weighting model proposed in the study. Funding Information: This research did not receive any specific external grant from funding agencies in the public, commercial, or not-for-profit sectors. The work was conducted as part of the author's professional responsibilities at the Agency for Sustainability and Climate Change (ASCC) and was supported by its institutional resources. The ASCC is a committee of the Corporación de Fomento de la Producción (CORFO). COLUMN DESCRIPTIONS 1. (Unnamed) Row index. 2. CODIGO_APL Unique identifier for the Clean Production Agreement (CPA). 3. BS Total Social Benefit, monetized in Chilean Unidad de Fomento (UF). 4. Año.Certificación Year of final certification of the CPA. 5. REGION_APL Primary region of implementation for the CPA. NACIONAL indicates a national scope. 6. GRANDE Number of participating facilities classified as 'Large Enterprise'. 7. MEDIANA Number of participating facilities classified as 'Medium Enterprise'. 8. MICRO Number of participating facilities classified as 'Micro Enterprise'. 9. NC: Number of participating facilities classified as 'Unclassified', primarily consisting of civil society organizations. 10. PEQUENA Number of participating facilities classified as 'Small Enterprise'. 11. SSPP: Number of participating facilities classified as 'Public Services' (e.g., government agencies, municipalities). 12. Total.general Total number of participating facilities in the CPA. 13. Tasa_MIPYME SME (Micro, Small, and Medium Enterprise) participation rate. 14. Gasto.21 Pro-rated share of the agency's personnel and human resource costs (Chilean public budget sub-item 21 Personnel Expenses), allocated to the CPA based on its activity duration, in UF. 15. Gasto.22 Pro-rated share of the agency's operational overhead costs (Chilean public budget sub-item 22 Goods and Services), allocated to the CPA based on its activity duration, in UF. 16. Gasto.24 Total direct funding allocated to the CPA under the Chilean public budget sub-item 24 (Current Transfers), in UF. 17. Gasto.Total Total public expenditure allocated to the CPA (sum of Gasto.21, Gasto.22, Gasto.24), in UF. 18. IDH_2022 Human Development Index (2022 estimate) for the corresponding region. 19. Ponderador_MIPYME The SME weighting component, calculated as (1 + Tasa_MIPYME). 20. Ponderador_IDH The territorial weighting component, calculated as (HDI_reference IDH_region).
Files
Steps to reproduce
Part 1: Data Gathering and Assembly The analytical dataset provided was created by integrating three primary public and institutional data sources from the Agency for Sustainability and Climate Change (ASCC) of Chile. The workflow was as follows: The 'Database of Social Benefits per CPA', the 'Master Register of Agreements', and the 'Master Register of Facilities' were merged using the unique identifier CODIGO_APL. The resulting dataset was filtered to include only CPAs signed from 2012 and certified by the end of 2024. The variable Tasa_MIPYME (SME Rate) was calculated based on the number of micro, small, and medium enterprises relative to the total number of facilities in each agreement. This consolidated data was then merged with subnational Human Development Index (HDI) data from Smits & Permanyer (2019). Part 2: Reproduction of Manuscript Results To reproduce the tables and figures in the associated manuscript: Software: R (version 4.0 or later) and RStudio are required. The following R packages must be installed: flextable, dplyr, ggplot2, tidyr, knitr, stringr, tidytext, rsvg. Setup: Download the analytical dataset (.csv), the R Markdown source code (.Rmd), the bibliography (.bib), and the flowchart image (.svg) and place them all in the same working directory. Execution: Open the .Rmd file in RStudio and "Knit" (render) the document. The embedded code will execute the full analysis and generate all results automatically. SOURCE INFORMATION This dataset is a processed compilation derived from the following primary sources - Social Benefit, Agreement, and Company Size Data Records from the Agencia de Sustentabilidad y Cambio Climático (ASCC), partially available through Chile's public data portal (datos.gob.cl). - Human Development Index (HDI) Data Smits, J. & Permanyer, I. (2019). The Sub-national Human Development Index. - Other economic and policy data as cited in the main article.