In the metal cutting industry, manufacturers have strived to increase energy efficiency and to reduce environmental burdens through the use of dust collectors and waste disposers. It is more beneficial and efficient to apply the front-of-pipe technology that prevents the sources of pollutants and minimises energy use through the redesign of products and the change of process planning and machining operations. In particular, process planning for the environment, called eco-process planning, is central to increasing energy efficiency and reducing environmental burdens because process planning decisions greatly influence machining performance. At present, greenability, a term used to indicate environmental friendliness, has been little considered as a major concern in the process planning stage because process planning decisions have focused on improving productivity aspects that include speed, cost and quality. Thus, it is essential to develop an eco-process planning approach that enables the harmonisation and enhancement of greenability performance while improving productivity performance, termed green productivity (GP). This paper presents the development of a GP-based process planning algorithm that enables the derivation of process parameters for improving GP in machining operations. The core mechanism of the algorithm is the realisation of the process improvement cycle that measures GP performance by the collection of machining data, quantifies this performance by categorical representation and predicts the performance through prediction models. To show the feasibility and applicability of the proposed algorithm, we have conducted an experiment and implemented a prototype system for a turning machining process.