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The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors.

The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases.

From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment.

These findings can help utilities to align model selection strategies with their risk tolerance.

To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

To be green creative is to come up with fresh, original and practical ideas for green products, green services, green processes or green activities. The purpose of this study is to provide a comprehensive overview of green creativity (GC) research by tracing the development of important theories, contexts, characteristics and methodologies (TCCM), and to illustrate how they relate to one another based on the systematic review and analysis of the existing literature relevant to GC from 2013 to 2023.

The research takes a methodical, structured approach to its literature evaluation, identifying prior contributions and offering frameworks for future study.

This research aims to highlight the challenges associated with planning, developing and implementing GC to realize the firm’s strategic and operational goals. Comprehensive networks, important countries, notable authors, key TCCM are provided by a TCCM and bibliographic analysis of the current GC literature.

The research addresses the concerns of managers across all types of entities and fills in the gaps, such as the skewed focus on GC’s applicability in large businesses and developing countries, as well as the limitations of a single-level analysis.

The research as a whole provides the taxonomy, utilization and mapping of logical concepts that strengthen GC. The study also highlights areas where more research is needed and where gaps and unresolved tensions remain. By delving into the nature of knowledge, the authors can better understand the factors that will ultimately shape the scope of future studies.