I think both conclusions are wrong. Since this is a meta-study, drawing conclusions from the absence of null-results and the absence of clustering is prone to

I think both conclusions are wrong. Since this is a meta-study, drawing conclusions from the absence of null-results and the absence of clustering is prone to a lot of confounders. The article mentions publication-bias, where non-null-results are published with a higher probability. Other such confounders will exist, e.g. data-availability-bias in certain places and populations, location-bias because researchers study their usually first-world, urban, university-town surroundings more often. Community-bias, because researchers tend to cluster and get inspiration for new studies from their community and peer group on the conferences they visit. Data-similarity-bias, because they can only compare studies where data can be normalized to a common base. Quality-bias, because they necessarily include well-done and badly-done studies, in different amounts, usually without weighing them properly, because that is very hard to do. Most of those confounders cannot be corrected for. So one could draw a conclusion like "we do see null-results in the expected amount and clustering in the expected amount, therefore the result of this meta-study is a confirmation of the lead-crime-hypothesis". But the only correct opposite conclusion is "we do not see null-results and clustering in the expected amount, so our result is 'we do not know'". So the imho correct interpretation of the meta-study is "don't know, further research or better meta-analysis needed".