How productive is liquid waste management practices in Indian informal micro, small and medium enterprises?
Bibliographic record
Abstract
Purpose Waste management is a crucial aspect of sustainable development, but is it economically sustainable for marginalized informal firms? The study tries to answer this question by revisiting the Porter–Wagner dilemma about the association between environmental management (EM) and firm performance (FP). The study looks into the various liquid waste management practices (LWMPs) adopted by them and the overall impact of LWMPs on firms' economic performance. Design/methodology/approach The study uses the latest available cross-sectional data source on Indian informal firms by the National Sample Survey Office (NSSO), 73rd survey round 2015–16. First, ordered logistic regression was used to analyse the factors that impact a firm's adoption of a particular LWMP. Subsequently, to capture the heterogeneity among the firms based on productivity and size, a quantile regression (QR) was employed to analyse the impact of LWMPs on firm productivity. Additionally, the propensity score matching technique was used to address endogeneity concerns. Findings The authors find that bigger, urban-located and female-owned firms adopt cleaner LWMPs that positively impact their economic performance. Furthermore, the QR analysis observed that the most productive firms could extract higher returns from adopting cleaner LWMPs, indicating the relevance of the Porter–Wagner dilemma, i.e. environmental and economic sustainability are possibly symbiotic, thus having a feedback mechanism. Originality/value To the authors’ limited knowledge, this is the first study analysing the relationship between EM and FP among the informal sector firms, which are away from any regulations or obligations. Since sustainability is a two-way process, policies should be devised that incentivise sustainable business practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".