Green bubbles: a four-stage paradigm for detection and propagation
Climate change has emerged as a significant global concern, attracting increasing attention worldwide. While green bubbles may be examined through a social bubble hypothesis, it is essential not to neglect a Climate Minsky moment triggered by sudden asset price changes. The significant increase in green investments highlights the urgent need for a comprehensive understanding of these market dynamics. Therefore, the current paper introduces a novel paradigm for studying such phenomena. Focusing on the renewable energy sector, Statistical Process Control (SPC) methodologies are employed to identify green bubbles within time series data. Furthermore, search volume indexes and social factors are incorporated into established econometric models to reveal potential implications for the financial system. Inspired by Joseph Schumpeter's perspectives on business cycles, this study recognizes green bubbles as a necessary evil for facilitating a successful transition towards a more sustainable future.
Code (0)
등록된 구현이 없습니다.
Tasks
Time SeriesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Bubble Detection with Application to Green Bubbles: A Noncausal Approach
This paper introduces a new approach to detect bubbles based on mixed causal and noncausal processes and their tail process representation during explosive episodes. Departing from traditional definitions of bubbles as n…
User-controllable Recommendation Against Filter Bubbles
Recommender systems usually face the issue of filter bubbles: overrecommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along the feedback loop and inadvertently narr…
BlockingcounterfactualCounterfactual InferenceDiversity+2A Lightweight Phenology-Aware YOLOv5 Framework for Tomato Growth Stage Detection in Resource-Constrained Bhutanese Greenhouse Environments
Accurate detection of tomato growth stages is essential for stage-specific greenhouse management and precision agriculture. In Bhutan, greenhouse cultivation is affected by altitude variability, large diurnal temperature…
Object DetectionGreenCOD: A Green Camouflaged Object Detection Method
We introduce GreenCOD, a green method for detecting camouflaged objects, distinct in its avoidance of backpropagation techniques. GreenCOD leverages gradient boosting and deep features extracted from pre-trained Deep Neu…
Objectobject-detectionObject DetectionDetection of Chinese Stock Market Bubbles with LPPLS Confidence Indicator
We present an advance bubble detection methodology based on the Log Periodic Power Law Singularity (LPPLS) confidence indicator for the early causal identification of positive and negative bubbles in the Chinese stock ma…
Causal Identification