Waste Management Clean Development Mechanism CAQCA NVivo 10(R) Dataset

Published: 6 July 2017| Version 1 | DOI: 10.17632/n4jn2wfvg5.1
Contributor:
André Bufoni

Description

This dataset contains one NVivo 10® file with the complete 432 projects design documents (PDD) of seven waste management sector industries registered as Clean Development Mechanism under UNFCCC Kyoto Protocol Initiative from 2004 to 2014. All data analyses and sample statistics during the research remain in the file. We coded PDDs in 890 fragments of text, classified in five categories of barriers (nodes): technological, financial, human resources, regulatory, socio-political. The data supports the findings of author thesis [1] and other two indexed publication in waste management journal [2,3]. The data allows any computer assisted qualitative content analysis (CAQCA) on the sector.

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The computer assisted qualitative content analysis (CAQCA) drives the datafile design. CAQCA is a well-known methodology largely used in social applied and human sciences [15–18]. The method involves the recursive procedure (coding) of a simple term search on the content to select the units for analysis (phenomenology) and, for those units, a semantic search, interpretation, and possible relationships among the fragments selected (hermeneutic) [19]. Bowen [20] highlights that the content analysis documents are also a source of a historical view, new hypotheses, modeling support, change and development tracking, and evidence triangulation. The use of software (qualitative data analysis software - QDAS) to treat a large volume of data produced almost in real time had a significant impact on research institutions and doctoral programs, because part of those research without the QDAS would be impractical and impossible. This fact explains why the more Universities offers the QDAS at an institutional level [21]. Furthermore, the development of specific software for content analysis made several searches and coding tools, statistics for hypothesis testing and concepts development available, that, in turn, have a significant impact on the research results [16,22]. The search criteria used to the content selection (raw data) from the UNFCCC Project Search site [10] in this file is in Figure 1. Sectoral Scope: Waste handling and disposal (13), Scale: Large, Status: Registered, Sort by: Reference number. The original studies supported by the data aimed the waste management sector barriers, but we can extend it to any other waste management projects aspect not only the barriers. Only large projects (432 of 923) were selected because only large projects have to full disclosure technical and financial information about the initiatives, what makes the sample more useful. The projects have many possible statuses (requesting registrations, deregistered, withdraw, rejected, revision, applied for, and corrections). We choose the registered status because of no other guarantees UNFCCC consent for the registered operation. At last, the sort by reference number is strategic considering that the computer database key-index system is more reliable then registration date. After the simple search for the term ‘barrier’ that returned more than a thousand occurrences, took place the coding second part by semantic-hermeneutic analysis of fragments. Some of these fragments proof meaningless and discarded. The rest classified in categories for future interpretation and pattern identification.

Institutions

Universidade Federal do Rio de Janeiro

Categories

Content Analysis, Waste, Clean Development Mechanism, Project Success, Qualitative Analysis

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