# Research on emission reduction investment strategies for low carbon technology enterprises

## TL;DR

This paper explores how companies can best invest in low-carbon technologies by using a flexible decision-making model that accounts for uncertainties.

## Contribution

The study introduces a fuzzy real options framework to optimize emission reduction investments under technological and regulatory uncertainties.

## Key findings

- The fuzzy option model outperforms traditional NPV methods in valuing managerial flexibility.
- Modular implementation strategies enhance adaptive capacity and incremental value in emission mitigation.
- Improved technology and regulatory factors lower capital deployment thresholds for sustainable investments.

## Abstract

This study investigates optimal capital allocation strategies for corporate decarbonization initiatives under technological transition constraints. A decision analysis framework grounded in real options theory is developed, incorporating fuzzy-set parameters to address implementation uncertainties in emission mitigation systems. Analytical results demonstrate inverse correlations between critical intervention factors (technological decarbonization efficiency, environmental taxation levels, eco-product market premiums, and fiscal incentive mechanisms) and capital deployment thresholds. Improved technical specifications, reinforced regulatory constraints, positive consumer responses, and targeted subsidy mechanisms synergistically facilitate sustainable infrastructure investments. Comparative evaluations confirm the proposed fuzzy option model’s superiority over conventional NPV methods in valuing managerial flexibility and mitigating valuation biases. Sequential option analysis reveals that modular implementation approaches can generate incremental value through adaptive capacity in operational execution. Empirical validation through ‘’industrial case studies illustrate the framework’s practical efficacy in assessing sustainable technology portfolios, offering actionable insights for strategic planning in carbon-intensive industries. This research contributes methodological advancements for timing optimization and risk assessment in environmental technology adoption scenarios.