diff --git a/scripts/v.dissolve/tests/v_dissolve_aggregate_test.py b/scripts/v.dissolve/tests/v_dissolve_aggregate_test.py
index 1fbc6310b05..2d273ce5b27 100644
--- a/scripts/v.dissolve/tests/v_dissolve_aggregate_test.py
+++ b/scripts/v.dissolve/tests/v_dissolve_aggregate_test.py
@@ -179,7 +179,7 @@ def test_aggregate_column_result(dataset, backend):
 
 
 def test_sqlite_agg_accepted(dataset):
-    """Numeric SQLite aggregate function are accepted
+    """Numeric SQLite aggregate functions are accepted
 
     Additionally, it checks:
     1. generated column names
@@ -244,6 +244,33 @@ def test_sqlite_agg_accepted(dataset):
     assert sorted(aggregate_n) == [1, 2, 3]
 
 
+def test_sqlite_concat(dataset):
+    """SQLite concat text-returning aggregate function works"""
+    dissolved_vector = "test_sqlite_concat"
+    gs.run_command(
+        "v.dissolve",
+        input=dataset.vector_name,
+        column=dataset.str_column_name,
+        output=dissolved_vector,
+        aggregate_column=f"group_concat({dataset.int_column_name})",
+        result_column="concat_values text",
+        aggregate_backend="sql",
+    )
+    records = json.loads(
+        gs.read_command(
+            "v.db.select",
+            map=dissolved_vector,
+            format="json",
+        )
+    )["records"]
+    # Order of records is ignored - they are just sorted.
+    # Order within values of group_concat is defined as arbitrary by SQLite.
+    expected_integers = sorted(["10", "10,10,24", "5,5"])
+    actual_integers = sorted([record["concat_values"] for record in records])
+    for expected, actual in zip(expected_integers, actual_integers):
+        assert sorted(expected.split(",")) == sorted(actual.split(","))
+
+
 def test_duplicate_columns_and_methods_accepted(dataset):
     """Duplicate aggregate columns and methods are accepted and deduplicated"""
     dissolved_vector = "test_duplicates"
diff --git a/scripts/v.dissolve/v.dissolve.py b/scripts/v.dissolve/v.dissolve.py
index 1192607d1a6..e8e65089cbe 100755
--- a/scripts/v.dissolve/v.dissolve.py
+++ b/scripts/v.dissolve/v.dissolve.py
@@ -153,12 +153,33 @@ def modify_methods_for_backend(methods, backend):
     return new_methods
 
 
+def quote_from_type(column_type):
+    """Returns quote if column values need to be quoted based on their type
+
+    Defaults to quoting for unknown types and no quoting for falsely values,
+    i.e., unknown types are assumed to be in need of quoting while missing type
+    information is assumed to be associated with numbers which don't need quoting.
+    """
+    # Needs a general solution, e.g., https://github.com/OSGeo/grass/pull/1110
+    if not column_type or column_type.upper() in [
+        "INT",
+        "INTEGER",
+        "SMALLINT",
+        "REAL",
+        "DOUBLE",
+        "DOUBLE PRECISION",
+    ]:
+        return ""
+    return "'"
+
+
 def updates_to_sql(table, updates):
     """Create SQL from a list of dicts with column, value, where"""
     sql = ["BEGIN TRANSACTION"]
     for update in updates:
+        quote = quote_from_type(update.get("type", None))
         sql.append(
-            f"UPDATE {table} SET {update['column']} = {update['value']} "
+            f"UPDATE {table} SET {update['column']} = {quote}{update['value']}{quote} "
             f"WHERE {update['where']};"
         )
     sql.append("END TRANSACTION")
@@ -215,6 +236,27 @@ def check_aggregate_methods_or_fatal(methods, backend):
     # and open for SQLite depending on its extensions.
 
 
+def aggregate_columns_exist_or_fatal(vector, layer, columns):
+    """Check that all columns exist or end with fatal error"""
+    column_names = gs.vector_columns(vector, layer).keys()
+    for column in columns:
+        if column not in column_names:
+            if "(" in column:
+                gs.fatal(
+                    _(
+                        "Column <{column}> does not exist in vector <{vector}> "
+                        "(layer <{layer}>). Specify result columns if you are adding "
+                        "function calls to aggregate columns."
+                    ).format(vector=vector, layer=layer, column=column)
+                )
+            gs.fatal(
+                _(
+                    "Column <{column}> selected for aggregation does not exist "
+                    "in vector <{vector}> (layer <{layer}>)"
+                ).format(vector=vector, layer=layer, column=column)
+            )
+
+
 def match_columns_and_methods(columns, methods):
     """Return all combinations of columns and methods
 
@@ -351,19 +393,26 @@ def aggregate_attributes_sql(
         for result_column, column_type in zip(result_columns, column_types):
             add_columns.append(f"{result_column} {column_type}")
     else:
-        add_columns = result_columns.copy()
+        # Column types are part of the result column name list.
+        add_columns = result_columns.copy()  # Ensure we have our own copy.
+        # Split column definitions into two lists.
+        result_columns = []
+        column_types = []
+        for definition in add_columns:
+            column_name, column_type = definition.split(" ", maxsplit=1)
+            result_columns.append(column_name)
+            column_types.append(column_type)
     for row in records:
         where = column_value_to_where(column, row[column], quote=quote_column)
         for (
             result_column,
+            column_type,
             key,
-        ) in zip(result_columns, select_columns):
-            if not column_types:
-                # Column types are part of the result column name list.
-                result_column = result_column.split(" ", maxsplit=1)[0]
+        ) in zip(result_columns, column_types, select_columns):
             updates.append(
                 {
                     "column": result_column,
+                    "type": column_type,
                     "value": row[key],
                     "where": where,
                 }
@@ -470,6 +519,7 @@ def main():
         user_aggregate_methods, aggregate_backend, provide_defaults=not result_columns
     )
     if not result_columns:
+        aggregate_columns_exist_or_fatal(input_vector, layer, columns_to_aggregate)
         columns_to_aggregate, user_aggregate_methods = match_columns_and_methods(
             columns_to_aggregate, user_aggregate_methods
         )