Industrial green development (IGD) is fundamental for curbing serious pollution and over-consumption of natural resources caused by industrial growth, while quantification of the IGD level has become instrumental in guiding further progress. However, three existing research gaps prevent a comprehensive and accurate evaluation of the IGD level. These gaps include the ignorance of the underlying conditions, lack of spatio-temporal comparability in the IGD indicator system, and absence of more reasonable assumptions within the technique for order preference by similarity to an ideal solution (TOPSIS) and slack-based measure (SBM) methods. The main goals of this study are to propose an improved evaluation framework to fill these gaps and to provide new knowledge by analyzing the IGD status, weaknesses, and spatio-temporal differences. With the collected panel data across 30 provinces in China from 2006 to 2015, the following findings were revealed based on the proposed evaluation framework: 1) both the performance and quality of IGD increased but their growth rates had significantly declined in the later years; 2) pollution abatement had the maximum improvement, followed by low-carbon production, resource reduction, and economic operation; 3) coastal provinces had higher performance and quality of IGD than inland provinces, especially for low-carbon production and pure technical efficiency; 4) provinces with higher IGD performance usually had higher pure technical efficiency, but not scale efficiency; 5) the traditional evaluation framework either significantly overestimated or underestimated the IGD efforts in some provinces. Our findings highlight the importance of differentiated policies between coastal provinces and inland provinces or between high-IGD-performance/quality provinces and low-IGD-performance/quality provinces, as well as call for effective measures in response to slowing growth. Moreover, our methodology can comprehensively and accurately evaluate the IGD level by applying an extensive process considering underlying conditions/inputs, a spatio-temporal indicator system, and new integrated methods.