hello!

I have a panel dataset on which I would like to perform a regression. my dependent variable is the change in carbon intensity during the year, and my dependent variables are a number of firm characteristics, for 230 firms.

Companynumber year firmsize profitability leverage capitalintensity CAPEX KZindex pctcarbonintensitychange
2.442e+08 2013 18.7566 .0005925 .0765077 .3196759 .1901186 -.0483869 -.0484771
2.442e+08 2014 18.84879 .005932 .1401661 .2698615 .2208322 .1099032 -.0484771
2.442e+08 2015 18.7756 .0086093 .0432784 .2847554 .2438501 -.2025951 -.0253539
2.442e+08 2016 18.67199 .0370381 .03602 .3608946 .264327 -.4171754 -.0003668
2.442e+08 2017 18.74567 .0932389 .0619554 .4206807 .3075858 -.3962344 -.0053627
2.442e+08 2018 18.74328 .0089255 .0107424 .4338437 .2702132 -.4307564 -.0175255

To probe causality, I want to understand the effect of the firm characteristics of previous year on the percentage change in carbon intensity of the current year. If I perform an OLS on this data:
Code:
regress pctcarbonintensitychange L.firmsize L.profitability L.leverage L.capitalintensity  L.CAPEX L.KZindex
my R squared value is extremely low (0,02). This is striking because doing the same analysis with one wave data (regressing the firm characteristics of 2012 on the percentage change of carbon intensity between 2013 and 2018) I have an R squared value of 0,4664. I am now doubting whether an OLS regression is suitable for the goal of my regression.

However, I know performing a GEE could be the answer. This gives me also viable output. However, my knowledge on GEE is not profound and I would prefer using OLS in order to be sure to interpret the output correctly.

What is your view on this? do you think I could use an OLS regression? and if this is the case, what should be changed to my code in order for my regression to be more informative?

Kind regards,
Timea De Wispelaere