Understanding the local economic impacts of natural capital investment

Conclusion

This project has demonstrated that it is possible to quantify the typical contributions to local economies (measured as output effect and jobs created) of four different types of natural capital investment. This has been achieved through the development of a bespoke economic model using Scottish Input-Output (I-O) tables. Applying this model to the four natural capital investments considered should enable the increased inclusion of local economic impacts in business cases, investment strategies and policy decisions in the future. This will be vital if the anticipated investment in the restoration of natural capital across Scotland is to be achieved.

In addition, the project has highlighted current data caps and areas for further investigation which should allow the targeting of future research projects by Scottish Government to build upon the success of this project and unlock increased insight into the economic impacts and wider benefits of natural capital investment.

Next steps and recommendations

This project has shown that further collaboration will be required with the private sector in the future to improve the accuracy and resolution of the data and evidence available for future models. The feature within the model that allows users to update the reference values used within the model when more suitable values are available will results of to be incorporated.

The I-O model has been designed to allow for stakeholder engagement with private companies and those involved in the sector, who could provide much of the source information / reference data used within the models. This was particularly the case for the quantification of scale and cost associated with each activity.

An additional factor that was determined during the stakeholder engagement was that many of the skills needed to carry out the activities listed within the different interventions are underrepresented within the labour market. There is a requirement to review the required skills development programme linked to the activities within the different I-O models and ensure suitable upskilling programmes are available. In Scotland, this is currently being undertaken through initiatives such as the Skills Development Scotland (SDS) Climate Emergency Skills Action Plan (CESAP) 2020 – 2025^[11] and the NatureScot Nature-based Jobs and Skills Action Plan 2021-2022.^[12]

The results of the I-O models have shown that improvements to the SIC code taxonomy maybe required if the multipliers are to be more representative of the actual activities undertaken. Certain natural capital investment types such as woodland creation and regenerative agriculture benefit from the SIC code directly relatable to the activity, (i.e. 'Support Services to Forestry' and 'Mixed Farming'). However, for peatland restoration, where specialist civil engineering and plant operating skills are required, these were not easily captured within the SIC codes. Both situations risk the true value of the activity being incorrectly measured due to inaccurate multipliers.

It is recommended that a review of the relevant SIC codes is undertaken following this project and where required new codes that specifically relate to natural capital investment projects developed. This should be undertaken in partnership with the private sector to maximise their experience.

According to Green Book guidance, job impacts should be assessed for additionality. The main additionality indicators are leakage, displacement, substitution and displacement.^[13] A full additionality analysis was outside the scope of this study. However, we recommend that the job impacts are compared with the sector and skills profile of the region. Otherwise, any investment would lead to jobs leaking out of the region.

Finally, as, mentioned within the model assumptions section, the values within the I-O model are standard costs for the national scale. Further research and scientific adjustments can be undertaken to review case studies at various scales to improve the accuracy of the models and refine the costs within them.