Context. Measuring and optimizing software’s carbon footprint is methodologically challenging. While the Software Carbon Intensity (SCI) specification standardizes carbon measurement, most research emphasizes infrastructure- over application-level optimizations.

Goal. We evaluate carbon emission reduction strategies for an industrial software application by applying the SCI specification to quantify the impact of application-level configurations on software carbon emissions and identify effective optimizations.

Method. This study targets AFAS SB, a financial administration application developed in the Netherlands. Firstly, baseline carbon emissions are assessed from Microsoft Azure metrics collected in production. Then, in-the-lab experiments are conducted for quantifying how 18 application-level configurations impact the estimated carbon emissions of AFAS SB across 79 functional test scenarios.

Results. Garbage collection optimization reduced carbon emissions by 29.5–30.6%, while other configurations had minimal effect. Combining top parameters achieved a 25.7% reduction without compromising functionality. Correlation analysis linked disk I/O reads to higher emissions.

Conclusions. Our findings show that developers can reduce carbon footprint through application-level configuration changes, with SCI enabling standardized measurement of optimization effectiveness across scenarios.